create shoukou

deploy/fastdeploy/README.md

+../../docs/zh_CN/fastdeploy/README.md
\ No newline at end of file

deploy/fastdeploy/amlogic/a311d/README.md

+../../../../../docs/zh_CN/fastdeploy/amlogic/a311d/README.md
\ No newline at end of file

deploy/fastdeploy/amlogic/a311d/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+include_directories(${FastDeploy_INCLUDE_DIRS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
+
+set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
+
+install(TARGETS infer_demo DESTINATION ./)
+
+install(DIRECTORY models DESTINATION ./)
+install(DIRECTORY images DESTINATION ./)
+
+file(GLOB_RECURSE FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/lib*.so*)
+file(GLOB_RECURSE ALL_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/lib*.so*)
+list(APPEND ALL_LIBS ${FASTDEPLOY_LIBS})
+install(PROGRAMS ${ALL_LIBS} DESTINATION lib)
+
+file(GLOB ADB_TOOLS run_with_adb.sh)
+install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)

deploy/fastdeploy/amlogic/a311d/cpp/README.md

+../../../../../docs/zh_CN/fastdeploy/amlogic/a311d/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/amlogic/a311d/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "fastdeploy/vision.h"
+#include <string>
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+  fastdeploy::vision::EnableFlyCV();
+  fastdeploy::RuntimeOption option;
+  option.UseTimVX();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/quant_model "
+                 "path/to/image "
+                 "e.g ./infer_demo ./ResNet50_vd_quant ./test.jpeg"
+              << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  InitAndInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/amlogic/a311d/cpp/run_with_adb.sh

+#!/bin/bash
+HOST_SPACE=${PWD}
+echo ${HOST_SPACE}
+WORK_SPACE=/data/local/tmp/test
+
+# The first parameter represents the demo name
+DEMO_NAME=image_classification_demo
+if [ -n "$1" ]; then
+  DEMO_NAME=$1
+fi
+
+# The second parameter represents the model name
+MODEL_NAME=mobilenet_v1_fp32_224
+if [ -n "$2" ]; then
+  MODEL_NAME=$2
+fi
+
+# The third parameter indicates the name of the image to be tested
+IMAGE_NAME=0001.jpg
+if [ -n "$3" ]; then
+  IMAGE_NAME=$3
+fi
+
+# The fourth parameter represents the ID of the device
+ADB_DEVICE_NAME=
+if [ -n "$4" ]; then
+  ADB_DEVICE_NAME="-s $4"
+fi
+
+# Set the environment variables required during the running process
+EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
+
+EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
+
+# Please install adb, and DON'T run this in the docker.
+set -e
+adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
+adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
+
+# Upload the demo, librarys, model and test images to the device
+adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
+adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
+adb $ADB_DEVICE_NAME push models $WORK_SPACE
+adb $ADB_DEVICE_NAME push images $WORK_SPACE
+
+# Execute the deployment demo
+adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"

deploy/fastdeploy/android/.gitignore

+.DS_Store
+.idea
+.gradle
+.cxx
+cache
+build
+app/cache
+app/libs/fastdeploy*
+app/.cxx
+app/build
+app/src/main/assets/models/*
+app/.gradle
+app/.idea
+fastdeploy/cache
+fastdeploy/libs/fastdeploy*
+fastdeploy/.cxx
+fastdeploy/build
+fastdeploy/src/main/assets/models/*
+fastdeploy/.gradle
+fastdeploy/.idea

deploy/fastdeploy/android/README.md

+../../../docs/zh_CN/fastdeploy/android/README.md
\ No newline at end of file

deploy/fastdeploy/android/app/build.gradle

+import java.security.MessageDigest
+
+apply plugin: 'com.android.application'
+
+android {
+    compileSdk 28
+
+    defaultConfig {
+        applicationId 'com.baidu.paddle.fastdeploy.app.examples'
+        minSdkVersion 15
+        //noinspection ExpiredTargetSdkVersion
+        targetSdkVersion 28
+        versionCode 1
+        versionName "1.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+    }
+
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+
+}
+
+dependencies {
+    implementation fileTree(include: ['*.aar'], dir: 'libs')
+    implementation 'com.android.support:appcompat-v7:28.0.0'
+    //noinspection GradleDependency
+    implementation 'com.android.support.constraint:constraint-layout:1.1.3'
+    implementation 'com.android.support:design:28.0.0'
+    implementation 'org.jetbrains:annotations:15.0'
+    //noinspection GradleDependency
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+}
+
+def FD_MODEL = [
+        [
+                'src' : 'https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz',
+                'dest': 'src/main/assets/models'
+        ]
+]
+
+def FD_JAVA_SDK = [
+        [
+                'src' : 'https://bj.bcebos.com/fastdeploy/test/fastdeploy-android-sdk-latest-dev.aar',
+                'dest': 'libs'
+        ]
+]
+
+task downloadAndExtractModels(type: DefaultTask) {
+    doFirst {
+        println "Downloading and extracting fastdeploy models ..."
+    }
+    doLast {
+        String cachePath = "cache"
+        if (!file("${cachePath}").exists()) {
+            mkdir "${cachePath}"
+        }
+        FD_MODEL.eachWithIndex { model, index ->
+            MessageDigest messageDigest = MessageDigest.getInstance('MD5')
+            messageDigest.update(model.src.bytes)
+            String[] modelPaths = model.src.split("/")
+            String modelName = modelPaths[modelPaths.length - 1]
+            // Download the target model if not exists
+            boolean copyFiles = !file("${model.dest}").exists()
+            if (!file("${cachePath}/${modelName}").exists()) {
+                println "Downloading ${model.src} -> ${cachePath}/${modelName}"
+                ant.get(src: model.src, dest: file("${cachePath}/${modelName}"))
+                copyFiles = true
+            }
+            if (copyFiles) {
+                println "Coping ${cachePath}/${modelName} -> ${model.dest}"
+                copy {
+                    from tarTree("${cachePath}/${modelName}")
+                    into "${model.dest}"
+                }
+            }
+        }
+    }
+}
+
+task downloadAndExtractSDKs(type: DefaultTask) {
+    doFirst {
+        println "Downloading and extracting fastdeploy android java sdk ..."
+    }
+    doLast {
+        String cachePath = "cache"
+        if (!file("${cachePath}").exists()) {
+            mkdir "${cachePath}"
+        }
+        FD_JAVA_SDK.eachWithIndex { sdk, index ->
+            String[] sdkPaths = sdk.src.split("/")
+            String sdkName = sdkPaths[sdkPaths.length - 1]
+            // Download the target SDK if not exists
+            boolean copyFiles = !file("${sdk.dest}/${sdkName}").exists()
+            if (!file("${cachePath}/${sdkName}").exists()) {
+                println "Downloading ${sdk.src} -> ${cachePath}/${sdkName}"
+                ant.get(src: sdk.src, dest: file("${cachePath}/${sdkName}"))
+                copyFiles = true
+            }
+            if (copyFiles) {
+                println "Coping ${cachePath}/${sdkName} -> ${sdk.dest}/${sdkName}"
+                copy {
+                    from "${cachePath}/${sdkName}"
+                    into "${sdk.dest}"
+                }
+            }
+        }
+    }
+}
+
+preBuild.dependsOn downloadAndExtractSDKs
+preBuild.dependsOn downloadAndExtractModels
\ No newline at end of file

deploy/fastdeploy/android/app/proguard-rules.pro

+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile
\ No newline at end of file

deploy/fastdeploy/android/app/src/androidTest/java/com/baidu/paddle/fastdeploy/ExampleInstrumentedTest.java

+package com.baidu.paddle.fastdeploy;
+
+import android.content.Context;
+
+import androidx.test.platform.app.InstrumentationRegistry;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
+        assertEquals("com.baidu.paddle.fastdeploy", appContext.getPackageName());
+    }
+}
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/AndroidManifest.xml

+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.baidu.paddle.fastdeploy.app.examples">
+
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.CAMERA"/>
+    <uses-feature android:name="android.hardware.camera" />
+    <uses-feature android:name="android.hardware.camera.autofocus" />
+
+    <application
+        android:allowBackup="true"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:roundIcon="@mipmap/ic_launcher_round"
+        android:supportsRtl="true"
+        android:theme="@style/AppTheme">
+        <activity android:name=".classification.ClassificationMainActivity">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN"/>
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+        <activity
+            android:name=".classification.ClassificationSettingsActivity"
+            android:label="Settings">
+        </activity>
+    </application>
+
+</manifest>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/assets/labels/imagenet1k_label_list.txt

+0 tench, Tinca tinca
+1 goldfish, Carassius auratus
+2 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+3 tiger shark, Galeocerdo cuvieri
+4 hammerhead, hammerhead shark
+5 electric ray, crampfish, numbfish, torpedo
+6 stingray
+7 cock
+8 hen
+9 ostrich, Struthio camelus
+10 brambling, Fringilla montifringilla
+11 goldfinch, Carduelis carduelis
+12 house finch, linnet, Carpodacus mexicanus
+13 junco, snowbird
+14 indigo bunting, indigo finch, indigo bird, Passerina cyanea
+15 robin, American robin, Turdus migratorius
+16 bulbul
+17 jay
+18 magpie
+19 chickadee
+20 water ouzel, dipper
+21 kite
+22 bald eagle, American eagle, Haliaeetus leucocephalus
+23 vulture
+24 great grey owl, great gray owl, Strix nebulosa
+25 European fire salamander, Salamandra salamandra
+26 common newt, Triturus vulgaris
+27 eft
+28 spotted salamander, Ambystoma maculatum
+29 axolotl, mud puppy, Ambystoma mexicanum
+30 bullfrog, Rana catesbeiana
+31 tree frog, tree-frog
+32 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+33 loggerhead, loggerhead turtle, Caretta caretta
+34 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+35 mud turtle
+36 terrapin
+37 box turtle, box tortoise
+38 banded gecko
+39 common iguana, iguana, Iguana iguana
+40 American chameleon, anole, Anolis carolinensis
+41 whiptail, whiptail lizard
+42 agama
+43 frilled lizard, Chlamydosaurus kingi
+44 alligator lizard
+45 Gila monster, Heloderma suspectum
+46 green lizard, Lacerta viridis
+47 African chameleon, Chamaeleo chamaeleon
+48 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+49 African crocodile, Nile crocodile, Crocodylus niloticus
+50 American alligator, Alligator mississipiensis
+51 triceratops
+52 thunder snake, worm snake, Carphophis amoenus
+53 ringneck snake, ring-necked snake, ring snake
+54 hognose snake, puff adder, sand viper
+55 green snake, grass snake
+56 king snake, kingsnake
+57 garter snake, grass snake
+58 water snake
+59 vine snake
+60 night snake, Hypsiglena torquata
+61 boa constrictor, Constrictor constrictor
+62 rock python, rock snake, Python sebae
+63 Indian cobra, Naja naja
+64 green mamba
+65 sea snake
+66 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+67 diamondback, diamondback rattlesnake, Crotalus adamanteus
+68 sidewinder, horned rattlesnake, Crotalus cerastes
+69 trilobite
+70 harvestman, daddy longlegs, Phalangium opilio
+71 scorpion
+72 black and gold garden spider, Argiope aurantia
+73 barn spider, Araneus cavaticus
+74 garden spider, Aranea diademata
+75 black widow, Latrodectus mactans
+76 tarantula
+77 wolf spider, hunting spider
+78 tick
+79 centipede
+80 black grouse
+81 ptarmigan
+82 ruffed grouse, partridge, Bonasa umbellus
+83 prairie chicken, prairie grouse, prairie fowl
+84 peacock
+85 quail
+86 partridge
+87 African grey, African gray, Psittacus erithacus
+88 macaw
+89 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+90 lorikeet
+91 coucal
+92 bee eater
+93 hornbill
+94 hummingbird
+95 jacamar
+96 toucan
+97 drake
+98 red-breasted merganser, Mergus serrator
+99 goose
+100 black swan, Cygnus atratus
+101 tusker
+102 echidna, spiny anteater, anteater
+103 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+104 wallaby, brush kangaroo
+105 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+106 wombat
+107 jellyfish
+108 sea anemone, anemone
+109 brain coral
+110 flatworm, platyhelminth
+111 nematode, nematode worm, roundworm
+112 conch
+113 snail
+114 slug
+115 sea slug, nudibranch
+116 chiton, coat-of-mail shell, sea cradle, polyplacophore
+117 chambered nautilus, pearly nautilus, nautilus
+118 Dungeness crab, Cancer magister
+119 rock crab, Cancer irroratus
+120 fiddler crab
+121 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+122 American lobster, Northern lobster, Maine lobster, Homarus americanus
+123 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+124 crayfish, crawfish, crawdad, crawdaddy
+125 hermit crab
+126 isopod
+127 white stork, Ciconia ciconia
+128 black stork, Ciconia nigra
+129 spoonbill
+130 flamingo
+131 little blue heron, Egretta caerulea
+132 American egret, great white heron, Egretta albus
+133 bittern
+134 crane
+135 limpkin, Aramus pictus
+136 European gallinule, Porphyrio porphyrio
+137 American coot, marsh hen, mud hen, water hen, Fulica americana
+138 bustard
+139 ruddy turnstone, Arenaria interpres
+140 red-backed sandpiper, dunlin, Erolia alpina
+141 redshank, Tringa totanus
+142 dowitcher
+143 oystercatcher, oyster catcher
+144 pelican
+145 king penguin, Aptenodytes patagonica
+146 albatross, mollymawk
+147 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+148 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+149 dugong, Dugong dugon
+150 sea lion
+151 Chihuahua
+152 Japanese spaniel
+153 Maltese dog, Maltese terrier, Maltese
+154 Pekinese, Pekingese, Peke
+155 Shih-Tzu
+156 Blenheim spaniel
+157 papillon
+158 toy terrier
+159 Rhodesian ridgeback
+160 Afghan hound, Afghan
+161 basset, basset hound
+162 beagle
+163 bloodhound, sleuthhound
+164 bluetick
+165 black-and-tan coonhound
+166 Walker hound, Walker foxhound
+167 English foxhound
+168 redbone
+169 borzoi, Russian wolfhound
+170 Irish wolfhound
+171 Italian greyhound
+172 whippet
+173 Ibizan hound, Ibizan Podenco
+174 Norwegian elkhound, elkhound
+175 otterhound, otter hound
+176 Saluki, gazelle hound
+177 Scottish deerhound, deerhound
+178 Weimaraner
+179 Staffordshire bullterrier, Staffordshire bull terrier
+180 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+181 Bedlington terrier
+182 Border terrier
+183 Kerry blue terrier
+184 Irish terrier
+185 Norfolk terrier
+186 Norwich terrier
+187 Yorkshire terrier
+188 wire-haired fox terrier
+189 Lakeland terrier
+190 Sealyham terrier, Sealyham
+191 Airedale, Airedale terrier
+192 cairn, cairn terrier
+193 Australian terrier
+194 Dandie Dinmont, Dandie Dinmont terrier
+195 Boston bull, Boston terrier
+196 miniature schnauzer
+197 giant schnauzer
+198 standard schnauzer
+199 Scotch terrier, Scottish terrier, Scottie
+200 Tibetan terrier, chrysanthemum dog
+201 silky terrier, Sydney silky
+202 soft-coated wheaten terrier
+203 West Highland white terrier
+204 Lhasa, Lhasa apso
+205 flat-coated retriever
+206 curly-coated retriever
+207 golden retriever
+208 Labrador retriever
+209 Chesapeake Bay retriever
+210 German short-haired pointer
+211 vizsla, Hungarian pointer
+212 English setter
+213 Irish setter, red setter
+214 Gordon setter
+215 Brittany spaniel
+216 clumber, clumber spaniel
+217 English springer, English springer spaniel
+218 Welsh springer spaniel
+219 cocker spaniel, English cocker spaniel, cocker
+220 Sussex spaniel
+221 Irish water spaniel
+222 kuvasz
+223 schipperke
+224 groenendael
+225 malinois
+226 briard
+227 kelpie
+228 komondor
+229 Old English sheepdog, bobtail
+230 Shetland sheepdog, Shetland sheep dog, Shetland
+231 collie
+232 Border collie
+233 Bouvier des Flandres, Bouviers des Flandres
+234 Rottweiler
+235 German shepherd, German shepherd dog, German police dog, alsatian
+236 Doberman, Doberman pinscher
+237 miniature pinscher
+238 Greater Swiss Mountain dog
+239 Bernese mountain dog
+240 Appenzeller
+241 EntleBucher
+242 boxer
+243 bull mastiff
+244 Tibetan mastiff
+245 French bulldog
+246 Great Dane
+247 Saint Bernard, St Bernard
+248 Eskimo dog, husky
+249 malamute, malemute, Alaskan malamute
+250 Siberian husky
+251 dalmatian, coach dog, carriage dog
+252 affenpinscher, monkey pinscher, monkey dog
+253 basenji
+254 pug, pug-dog
+255 Leonberg
+256 Newfoundland, Newfoundland dog
+257 Great Pyrenees
+258 Samoyed, Samoyede
+259 Pomeranian
+260 chow, chow chow
+261 keeshond
+262 Brabancon griffon
+263 Pembroke, Pembroke Welsh corgi
+264 Cardigan, Cardigan Welsh corgi
+265 toy poodle
+266 miniature poodle
+267 standard poodle
+268 Mexican hairless
+269 timber wolf, grey wolf, gray wolf, Canis lupus
+270 white wolf, Arctic wolf, Canis lupus tundrarum
+271 red wolf, maned wolf, Canis rufus, Canis niger
+272 coyote, prairie wolf, brush wolf, Canis latrans
+273 dingo, warrigal, warragal, Canis dingo
+274 dhole, Cuon alpinus
+275 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+276 hyena, hyaena
+277 red fox, Vulpes vulpes
+278 kit fox, Vulpes macrotis
+279 Arctic fox, white fox, Alopex lagopus
+280 grey fox, gray fox, Urocyon cinereoargenteus
+281 tabby, tabby cat
+282 tiger cat
+283 Persian cat
+284 Siamese cat, Siamese
+285 Egyptian cat
+286 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+287 lynx, catamount
+288 leopard, Panthera pardus
+289 snow leopard, ounce, Panthera uncia
+290 jaguar, panther, Panthera onca, Felis onca
+291 lion, king of beasts, Panthera leo
+292 tiger, Panthera tigris
+293 cheetah, chetah, Acinonyx jubatus
+294 brown bear, bruin, Ursus arctos
+295 American black bear, black bear, Ursus americanus, Euarctos americanus
+296 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+297 sloth bear, Melursus ursinus, Ursus ursinus
+298 mongoose
+299 meerkat, mierkat
+300 tiger beetle
+301 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+302 ground beetle, carabid beetle
+303 long-horned beetle, longicorn, longicorn beetle
+304 leaf beetle, chrysomelid
+305 dung beetle
+306 rhinoceros beetle
+307 weevil
+308 fly
+309 bee
+310 ant, emmet, pismire
+311 grasshopper, hopper
+312 cricket
+313 walking stick, walkingstick, stick insect
+314 cockroach, roach
+315 mantis, mantid
+316 cicada, cicala
+317 leafhopper
+318 lacewing, lacewing fly
+319 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+320 damselfly
+321 admiral
+322 ringlet, ringlet butterfly
+323 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+324 cabbage butterfly
+325 sulphur butterfly, sulfur butterfly
+326 lycaenid, lycaenid butterfly
+327 starfish, sea star
+328 sea urchin
+329 sea cucumber, holothurian
+330 wood rabbit, cottontail, cottontail rabbit
+331 hare
+332 Angora, Angora rabbit
+333 hamster
+334 porcupine, hedgehog
+335 fox squirrel, eastern fox squirrel, Sciurus niger
+336 marmot
+337 beaver
+338 guinea pig, Cavia cobaya
+339 sorrel
+340 zebra
+341 hog, pig, grunter, squealer, Sus scrofa
+342 wild boar, boar, Sus scrofa
+343 warthog
+344 hippopotamus, hippo, river horse, Hippopotamus amphibius
+345 ox
+346 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+347 bison
+348 ram, tup
+349 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+350 ibex, Capra ibex
+351 hartebeest
+352 impala, Aepyceros melampus
+353 gazelle
+354 Arabian camel, dromedary, Camelus dromedarius
+355 llama
+356 weasel
+357 mink
+358 polecat, fitch, foulmart, foumart, Mustela putorius
+359 black-footed ferret, ferret, Mustela nigripes
+360 otter
+361 skunk, polecat, wood pussy
+362 badger
+363 armadillo
+364 three-toed sloth, ai, Bradypus tridactylus
+365 orangutan, orang, orangutang, Pongo pygmaeus
+366 gorilla, Gorilla gorilla
+367 chimpanzee, chimp, Pan troglodytes
+368 gibbon, Hylobates lar
+369 siamang, Hylobates syndactylus, Symphalangus syndactylus
+370 guenon, guenon monkey
+371 patas, hussar monkey, Erythrocebus patas
+372 baboon
+373 macaque
+374 langur
+375 colobus, colobus monkey
+376 proboscis monkey, Nasalis larvatus
+377 marmoset
+378 capuchin, ringtail, Cebus capucinus
+379 howler monkey, howler
+380 titi, titi monkey
+381 spider monkey, Ateles geoffroyi
+382 squirrel monkey, Saimiri sciureus
+383 Madagascar cat, ring-tailed lemur, Lemur catta
+384 indri, indris, Indri indri, Indri brevicaudatus
+385 Indian elephant, Elephas maximus
+386 African elephant, Loxodonta africana
+387 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+388 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+389 barracouta, snoek
+390 eel
+391 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+392 rock beauty, Holocanthus tricolor
+393 anemone fish
+394 sturgeon
+395 gar, garfish, garpike, billfish, Lepisosteus osseus
+396 lionfish
+397 puffer, pufferfish, blowfish, globefish
+398 abacus
+399 abaya
+400 academic gown, academic robe, judge's robe
+401 accordion, piano accordion, squeeze box
+402 acoustic guitar
+403 aircraft carrier, carrier, flattop, attack aircraft carrier
+404 airliner
+405 airship, dirigible
+406 altar
+407 ambulance
+408 amphibian, amphibious vehicle
+409 analog clock
+410 apiary, bee house
+411 apron
+412 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+413 assault rifle, assault gun
+414 backpack, back pack, knapsack, packsack, rucksack, haversack
+415 bakery, bakeshop, bakehouse
+416 balance beam, beam
+417 balloon
+418 ballpoint, ballpoint pen, ballpen, Biro
+419 Band Aid
+420 banjo
+421 bannister, banister, balustrade, balusters, handrail
+422 barbell
+423 barber chair
+424 barbershop
+425 barn
+426 barometer
+427 barrel, cask
+428 barrow, garden cart, lawn cart, wheelbarrow
+429 baseball
+430 basketball
+431 bassinet
+432 bassoon
+433 bathing cap, swimming cap
+434 bath towel
+435 bathtub, bathing tub, bath, tub
+436 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+437 beacon, lighthouse, beacon light, pharos
+438 beaker
+439 bearskin, busby, shako
+440 beer bottle
+441 beer glass
+442 bell cote, bell cot
+443 bib
+444 bicycle-built-for-two, tandem bicycle, tandem
+445 bikini, two-piece
+446 binder, ring-binder
+447 binoculars, field glasses, opera glasses
+448 birdhouse
+449 boathouse
+450 bobsled, bobsleigh, bob
+451 bolo tie, bolo, bola tie, bola
+452 bonnet, poke bonnet
+453 bookcase
+454 bookshop, bookstore, bookstall
+455 bottlecap
+456 bow
+457 bow tie, bow-tie, bowtie
+458 brass, memorial tablet, plaque
+459 brassiere, bra, bandeau
+460 breakwater, groin, groyne, mole, bulwark, seawall, jetty
+461 breastplate, aegis, egis
+462 broom
+463 bucket, pail
+464 buckle
+465 bulletproof vest
+466 bullet train, bullet
+467 butcher shop, meat market
+468 cab, hack, taxi, taxicab
+469 caldron, cauldron
+470 candle, taper, wax light
+471 cannon
+472 canoe
+473 can opener, tin opener
+474 cardigan
+475 car mirror
+476 carousel, carrousel, merry-go-round, roundabout, whirligig
+477 carpenter's kit, tool kit
+478 carton
+479 car wheel
+480 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+481 cassette
+482 cassette player
+483 castle
+484 catamaran
+485 CD player
+486 cello, violoncello
+487 cellular telephone, cellular phone, cellphone, cell, mobile phone
+488 chain
+489 chainlink fence
+490 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+491 chain saw, chainsaw
+492 chest
+493 chiffonier, commode
+494 chime, bell, gong
+495 china cabinet, china closet
+496 Christmas stocking
+497 church, church building
+498 cinema, movie theater, movie theatre, movie house, picture palace
+499 cleaver, meat cleaver, chopper
+500 cliff dwelling
+501 cloak
+502 clog, geta, patten, sabot
+503 cocktail shaker
+504 coffee mug
+505 coffeepot
+506 coil, spiral, volute, whorl, helix
+507 combination lock
+508 computer keyboard, keypad
+509 confectionery, confectionary, candy store
+510 container ship, containership, container vessel
+511 convertible
+512 corkscrew, bottle screw
+513 cornet, horn, trumpet, trump
+514 cowboy boot
+515 cowboy hat, ten-gallon hat
+516 cradle
+517 crane
+518 crash helmet
+519 crate
+520 crib, cot
+521 Crock Pot
+522 croquet ball
+523 crutch
+524 cuirass
+525 dam, dike, dyke
+526 desk
+527 desktop computer
+528 dial telephone, dial phone
+529 diaper, nappy, napkin
+530 digital clock
+531 digital watch
+532 dining table, board
+533 dishrag, dishcloth
+534 dishwasher, dish washer, dishwashing machine
+535 disk brake, disc brake
+536 dock, dockage, docking facility
+537 dogsled, dog sled, dog sleigh
+538 dome
+539 doormat, welcome mat
+540 drilling platform, offshore rig
+541 drum, membranophone, tympan
+542 drumstick
+543 dumbbell
+544 Dutch oven
+545 electric fan, blower
+546 electric guitar
+547 electric locomotive
+548 entertainment center
+549 envelope
+550 espresso maker
+551 face powder
+552 feather boa, boa
+553 file, file cabinet, filing cabinet
+554 fireboat
+555 fire engine, fire truck
+556 fire screen, fireguard
+557 flagpole, flagstaff
+558 flute, transverse flute
+559 folding chair
+560 football helmet
+561 forklift
+562 fountain
+563 fountain pen
+564 four-poster
+565 freight car
+566 French horn, horn
+567 frying pan, frypan, skillet
+568 fur coat
+569 garbage truck, dustcart
+570 gasmask, respirator, gas helmet
+571 gas pump, gasoline pump, petrol pump, island dispenser
+572 goblet
+573 go-kart
+574 golf ball
+575 golfcart, golf cart
+576 gondola
+577 gong, tam-tam
+578 gown
+579 grand piano, grand
+580 greenhouse, nursery, glasshouse
+581 grille, radiator grille
+582 grocery store, grocery, food market, market
+583 guillotine
+584 hair slide
+585 hair spray
+586 half track
+587 hammer
+588 hamper
+589 hand blower, blow dryer, blow drier, hair dryer, hair drier
+590 hand-held computer, hand-held microcomputer
+591 handkerchief, hankie, hanky, hankey
+592 hard disc, hard disk, fixed disk
+593 harmonica, mouth organ, harp, mouth harp
+594 harp
+595 harvester, reaper
+596 hatchet
+597 holster
+598 home theater, home theatre
+599 honeycomb
+600 hook, claw
+601 hoopskirt, crinoline
+602 horizontal bar, high bar
+603 horse cart, horse-cart
+604 hourglass
+605 iPod
+606 iron, smoothing iron
+607 jack-o'-lantern
+608 jean, blue jean, denim
+609 jeep, landrover
+610 jersey, T-shirt, tee shirt
+611 jigsaw puzzle
+612 jinrikisha, ricksha, rickshaw
+613 joystick
+614 kimono
+615 knee pad
+616 knot
+617 lab coat, laboratory coat
+618 ladle
+619 lampshade, lamp shade
+620 laptop, laptop computer
+621 lawn mower, mower
+622 lens cap, lens cover
+623 letter opener, paper knife, paperknife
+624 library
+625 lifeboat
+626 lighter, light, igniter, ignitor
+627 limousine, limo
+628 liner, ocean liner
+629 lipstick, lip rouge
+630 Loafer
+631 lotion
+632 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+633 loupe, jeweler's loupe
+634 lumbermill, sawmill
+635 magnetic compass
+636 mailbag, postbag
+637 mailbox, letter box
+638 maillot
+639 maillot, tank suit
+640 manhole cover
+641 maraca
+642 marimba, xylophone
+643 mask
+644 matchstick
+645 maypole
+646 maze, labyrinth
+647 measuring cup
+648 medicine chest, medicine cabinet
+649 megalith, megalithic structure
+650 microphone, mike
+651 microwave, microwave oven
+652 military uniform
+653 milk can
+654 minibus
+655 miniskirt, mini
+656 minivan
+657 missile
+658 mitten
+659 mixing bowl
+660 mobile home, manufactured home
+661 Model T
+662 modem
+663 monastery
+664 monitor
+665 moped
+666 mortar
+667 mortarboard
+668 mosque
+669 mosquito net
+670 motor scooter, scooter
+671 mountain bike, all-terrain bike, off-roader
+672 mountain tent
+673 mouse, computer mouse
+674 mousetrap
+675 moving van
+676 muzzle
+677 nail
+678 neck brace
+679 necklace
+680 nipple
+681 notebook, notebook computer
+682 obelisk
+683 oboe, hautboy, hautbois
+684 ocarina, sweet potato
+685 odometer, hodometer, mileometer, milometer
+686 oil filter
+687 organ, pipe organ
+688 oscilloscope, scope, cathode-ray oscilloscope, CRO
+689 overskirt
+690 oxcart
+691 oxygen mask
+692 packet
+693 paddle, boat paddle
+694 paddlewheel, paddle wheel
+695 padlock
+696 paintbrush
+697 pajama, pyjama, pj's, jammies
+698 palace
+699 panpipe, pandean pipe, syrinx
+700 paper towel
+701 parachute, chute
+702 parallel bars, bars
+703 park bench
+704 parking meter
+705 passenger car, coach, carriage
+706 patio, terrace
+707 pay-phone, pay-station
+708 pedestal, plinth, footstall
+709 pencil box, pencil case
+710 pencil sharpener
+711 perfume, essence
+712 Petri dish
+713 photocopier
+714 pick, plectrum, plectron
+715 pickelhaube
+716 picket fence, paling
+717 pickup, pickup truck
+718 pier
+719 piggy bank, penny bank
+720 pill bottle
+721 pillow
+722 ping-pong ball
+723 pinwheel
+724 pirate, pirate ship
+725 pitcher, ewer
+726 plane, carpenter's plane, woodworking plane
+727 planetarium
+728 plastic bag
+729 plate rack
+730 plow, plough
+731 plunger, plumber's helper
+732 Polaroid camera, Polaroid Land camera
+733 pole
+734 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+735 poncho
+736 pool table, billiard table, snooker table
+737 pop bottle, soda bottle
+738 pot, flowerpot
+739 potter's wheel
+740 power drill
+741 prayer rug, prayer mat
+742 printer
+743 prison, prison house
+744 projectile, missile
+745 projector
+746 puck, hockey puck
+747 punching bag, punch bag, punching ball, punchball
+748 purse
+749 quill, quill pen
+750 quilt, comforter, comfort, puff
+751 racer, race car, racing car
+752 racket, racquet
+753 radiator
+754 radio, wireless
+755 radio telescope, radio reflector
+756 rain barrel
+757 recreational vehicle, RV, R.V.
+758 reel
+759 reflex camera
+760 refrigerator, icebox
+761 remote control, remote
+762 restaurant, eating house, eating place, eatery
+763 revolver, six-gun, six-shooter
+764 rifle
+765 rocking chair, rocker
+766 rotisserie
+767 rubber eraser, rubber, pencil eraser
+768 rugby ball
+769 rule, ruler
+770 running shoe
+771 safe
+772 safety pin
+773 saltshaker, salt shaker
+774 sandal
+775 sarong
+776 sax, saxophone
+777 scabbard
+778 scale, weighing machine
+779 school bus
+780 schooner
+781 scoreboard
+782 screen, CRT screen
+783 screw
+784 screwdriver
+785 seat belt, seatbelt
+786 sewing machine
+787 shield, buckler
+788 shoe shop, shoe-shop, shoe store
+789 shoji
+790 shopping basket
+791 shopping cart
+792 shovel
+793 shower cap
+794 shower curtain
+795 ski
+796 ski mask
+797 sleeping bag
+798 slide rule, slipstick
+799 sliding door
+800 slot, one-armed bandit
+801 snorkel
+802 snowmobile
+803 snowplow, snowplough
+804 soap dispenser
+805 soccer ball
+806 sock
+807 solar dish, solar collector, solar furnace
+808 sombrero
+809 soup bowl
+810 space bar
+811 space heater
+812 space shuttle
+813 spatula
+814 speedboat
+815 spider web, spider's web
+816 spindle
+817 sports car, sport car
+818 spotlight, spot
+819 stage
+820 steam locomotive
+821 steel arch bridge
+822 steel drum
+823 stethoscope
+824 stole
+825 stone wall
+826 stopwatch, stop watch
+827 stove
+828 strainer
+829 streetcar, tram, tramcar, trolley, trolley car
+830 stretcher
+831 studio couch, day bed
+832 stupa, tope
+833 submarine, pigboat, sub, U-boat
+834 suit, suit of clothes
+835 sundial
+836 sunglass
+837 sunglasses, dark glasses, shades
+838 sunscreen, sunblock, sun blocker
+839 suspension bridge
+840 swab, swob, mop
+841 sweatshirt
+842 swimming trunks, bathing trunks
+843 swing
+844 switch, electric switch, electrical switch
+845 syringe
+846 table lamp
+847 tank, army tank, armored combat vehicle, armoured combat vehicle
+848 tape player
+849 teapot
+850 teddy, teddy bear
+851 television, television system
+852 tennis ball
+853 thatch, thatched roof
+854 theater curtain, theatre curtain
+855 thimble
+856 thresher, thrasher, threshing machine
+857 throne
+858 tile roof
+859 toaster
+860 tobacco shop, tobacconist shop, tobacconist
+861 toilet seat
+862 torch
+863 totem pole
+864 tow truck, tow car, wrecker
+865 toyshop
+866 tractor
+867 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+868 tray
+869 trench coat
+870 tricycle, trike, velocipede
+871 trimaran
+872 tripod
+873 triumphal arch
+874 trolleybus, trolley coach, trackless trolley
+875 trombone
+876 tub, vat
+877 turnstile
+878 typewriter keyboard
+879 umbrella
+880 unicycle, monocycle
+881 upright, upright piano
+882 vacuum, vacuum cleaner
+883 vase
+884 vault
+885 velvet
+886 vending machine
+887 vestment
+888 viaduct
+889 violin, fiddle
+890 volleyball
+891 waffle iron
+892 wall clock
+893 wallet, billfold, notecase, pocketbook
+894 wardrobe, closet, press
+895 warplane, military plane
+896 washbasin, handbasin, washbowl, lavabo, wash-hand basin
+897 washer, automatic washer, washing machine
+898 water bottle
+899 water jug
+900 water tower
+901 whiskey jug
+902 whistle
+903 wig
+904 window screen
+905 window shade
+906 Windsor tie
+907 wine bottle
+908 wing
+909 wok
+910 wooden spoon
+911 wool, woolen, woollen
+912 worm fence, snake fence, snake-rail fence, Virginia fence
+913 wreck
+914 yawl
+915 yurt
+916 web site, website, internet site, site
+917 comic book
+918 crossword puzzle, crossword
+919 street sign
+920 traffic light, traffic signal, stoplight
+921 book jacket, dust cover, dust jacket, dust wrapper
+922 menu
+923 plate
+924 guacamole
+925 consomme
+926 hot pot, hotpot
+927 trifle
+928 ice cream, icecream
+929 ice lolly, lolly, lollipop, popsicle
+930 French loaf
+931 bagel, beigel
+932 pretzel
+933 cheeseburger
+934 hotdog, hot dog, red hot
+935 mashed potato
+936 head cabbage
+937 broccoli
+938 cauliflower
+939 zucchini, courgette
+940 spaghetti squash
+941 acorn squash
+942 butternut squash
+943 cucumber, cuke
+944 artichoke, globe artichoke
+945 bell pepper
+946 cardoon
+947 mushroom
+948 Granny Smith
+949 strawberry
+950 orange
+951 lemon
+952 fig
+953 pineapple, ananas
+954 banana
+955 jackfruit, jak, jack
+956 custard apple
+957 pomegranate
+958 hay
+959 carbonara
+960 chocolate sauce, chocolate syrup
+961 dough
+962 meat loaf, meatloaf
+963 pizza, pizza pie
+964 potpie
+965 burrito
+966 red wine
+967 espresso
+968 cup
+969 eggnog
+970 alp
+971 bubble
+972 cliff, drop, drop-off
+973 coral reef
+974 geyser
+975 lakeside, lakeshore
+976 promontory, headland, head, foreland
+977 sandbar, sand bar
+978 seashore, coast, seacoast, sea-coast
+979 valley, vale
+980 volcano
+981 ballplayer, baseball player
+982 groom, bridegroom
+983 scuba diver
+984 rapeseed
+985 daisy
+986 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+987 corn
+988 acorn
+989 hip, rose hip, rosehip
+990 buckeye, horse chestnut, conker
+991 coral fungus
+992 agaric
+993 gyromitra
+994 stinkhorn, carrion fungus
+995 earthstar
+996 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+997 bolete
+998 ear, spike, capitulum
+999 toilet tissue, toilet paper, bathroom tissue
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/classification/ClassificationMainActivity.java

+package com.baidu.paddle.fastdeploy.app.examples.classification;
+
+import static com.baidu.paddle.fastdeploy.app.ui.Utils.decodeBitmap;
+import static com.baidu.paddle.fastdeploy.app.ui.Utils.getRealPathFromURI;
+import static com.baidu.paddle.fastdeploy.app.ui.Utils.readTxt;
+
+import android.Manifest;
+import android.annotation.SuppressLint;
+import android.app.Activity;
+import android.app.AlertDialog;
+import android.content.DialogInterface;
+import android.content.Intent;
+import android.content.SharedPreferences;
+import android.content.pm.PackageManager;
+import android.graphics.Bitmap;
+import android.net.Uri;
+import android.os.Bundle;
+import android.os.SystemClock;
+import android.preference.PreferenceManager;
+import android.support.annotation.NonNull;
+import android.support.v4.app.ActivityCompat;
+import android.support.v4.content.ContextCompat;
+import android.util.Log;
+import android.view.View;
+import android.view.ViewGroup;
+import android.view.Window;
+import android.view.WindowManager;
+import android.widget.ImageButton;
+import android.widget.ImageView;
+import android.widget.SeekBar;
+import android.widget.TextView;
+
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.app.examples.R;
+import com.baidu.paddle.fastdeploy.app.ui.view.CameraSurfaceView;
+import com.baidu.paddle.fastdeploy.app.ui.view.ResultListView;
+import com.baidu.paddle.fastdeploy.app.ui.Utils;
+import com.baidu.paddle.fastdeploy.app.ui.view.adapter.BaseResultAdapter;
+import com.baidu.paddle.fastdeploy.app.ui.view.model.BaseResultModel;
+import com.baidu.paddle.fastdeploy.vision.ClassifyResult;
+import com.baidu.paddle.fastdeploy.vision.Visualize;
+import com.baidu.paddle.fastdeploy.vision.classification.PaddleClasModel;
+
+import java.math.BigDecimal;
+import java.util.ArrayList;
+import java.util.List;
+
+public class ClassificationMainActivity extends Activity implements View.OnClickListener, CameraSurfaceView.OnTextureChangedListener {
+    private static final String TAG = ClassificationMainActivity.class.getSimpleName();
+
+    CameraSurfaceView svPreview;
+    TextView tvStatus;
+    ImageButton btnSwitch;
+    ImageButton btnShutter;
+    ImageButton btnSettings;
+    ImageView realtimeToggleButton;
+    boolean isRealtimeStatusRunning = false;
+    ImageView backInPreview;
+    private ImageView albumSelectButton;
+    private View cameraPageView;
+    private ViewGroup resultPageView;
+    private ImageView resultImage;
+    private ImageView backInResult;
+    private SeekBar confidenceSeekbar;
+    private TextView seekbarText;
+    private float resultNum = 1.0f;
+    private ResultListView resultView;
+    private Bitmap picBitmap;
+    private Bitmap shutterBitmap;
+    private Bitmap originPicBitmap;
+    private Bitmap originShutterBitmap;
+    private boolean isShutterBitmapCopied = false;
+
+    public static final int TYPE_UNKNOWN = -1;
+    public static final int BTN_SHUTTER = 0;
+    public static final int ALBUM_SELECT = 1;
+    public static final int REALTIME_DETECT = 2;
+    private static int TYPE = REALTIME_DETECT;
+
+    private static final int REQUEST_PERMISSION_CODE_STORAGE = 101;
+    private static final int INTENT_CODE_PICK_IMAGE = 100;
+    private static final int TIME_SLEEP_INTERVAL = 50; // ms
+
+    long timeElapsed = 0;
+    long frameCounter = 0;
+
+    // Call 'init' and 'release' manually later
+    PaddleClasModel predictor = new PaddleClasModel();
+
+    private float[] scores;
+    private int[] labelId;
+    private boolean initialized;
+    private List<String> labelText;
+    private List<BaseResultModel> results = new ArrayList<>();
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+
+        // Fullscreen
+        requestWindowFeature(Window.FEATURE_NO_TITLE);
+        getWindow().setFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN, WindowManager.LayoutParams.FLAG_FULLSCREEN);
+
+        setContentView(R.layout.classification_activity_main);
+
+        // Clear all setting items to avoid app crashing due to the incorrect settings
+        initSettings();
+
+        // Check and request CAMERA and WRITE_EXTERNAL_STORAGE permissions
+        if (!checkAllPermissions()) {
+            requestAllPermissions();
+        }
+
+        // Init the camera preview and UI components
+        initView();
+    }
+
+    @SuppressLint("NonConstantResourceId")
+    @Override
+    public void onClick(View v) {
+        switch (v.getId()) {
+            case R.id.btn_switch:
+                svPreview.switchCamera();
+                break;
+            case R.id.btn_shutter:
+                TYPE = BTN_SHUTTER;
+                shutterAndPauseCamera();
+                resultView.setAdapter(null);
+                break;
+            case R.id.btn_settings:
+                startActivity(new Intent(ClassificationMainActivity.this, ClassificationSettingsActivity.class));
+                break;
+            case R.id.realtime_toggle_btn:
+                toggleRealtimeStyle();
+                break;
+            case R.id.back_in_preview:
+                finish();
+                break;
+            case R.id.iv_select:
+                TYPE = ALBUM_SELECT;
+                // Judge whether authority has been granted.
+                if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {
+                    // If this permission was requested before the application but the user refused the request, this method will return true.
+                    ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE}, REQUEST_PERMISSION_CODE_STORAGE);
+                } else {
+                    Intent intent = new Intent(Intent.ACTION_PICK);
+                    intent.setType("image/*");
+                    startActivityForResult(intent, INTENT_CODE_PICK_IMAGE);
+                }
+                resultView.setAdapter(null);
+                break;
+            case R.id.back_in_result:
+                back();
+                break;
+
+        }
+    }
+
+    @Override
+    public void onBackPressed() {
+        super.onBackPressed();
+        back();
+    }
+
+    private void back() {
+        resultPageView.setVisibility(View.GONE);
+        cameraPageView.setVisibility(View.VISIBLE);
+        TYPE = REALTIME_DETECT;
+        isShutterBitmapCopied = false;
+        svPreview.onResume();
+        results.clear();
+        if (scores != null) {
+            scores = null;
+        }
+        if (labelId != null) {
+            labelId = null;
+        }
+    }
+
+    private void shutterAndPauseCamera() {
+        new Thread(new Runnable() {
+            @Override
+            public void run() {
+                try {
+                    // Sleep some times to ensure picture has been correctly shut.
+                    Thread.sleep(TIME_SLEEP_INTERVAL * 10); // 500ms
+                } catch (InterruptedException e) {
+                    e.printStackTrace();
+                }
+                runOnUiThread(new Runnable() {
+                    @SuppressLint("SetTextI18n")
+                    public void run() {
+                        // These code will run in main thread.
+                        svPreview.onPause();
+                        cameraPageView.setVisibility(View.GONE);
+                        resultPageView.setVisibility(View.VISIBLE);
+                        seekbarText.setText(resultNum + "");
+                        confidenceSeekbar.setProgress((int) (resultNum * 100));
+                        if (shutterBitmap != null && !shutterBitmap.isRecycled()) {
+                            resultImage.setImageBitmap(shutterBitmap);
+                        } else {
+                            new AlertDialog.Builder(ClassificationMainActivity.this)
+                                    .setTitle("Empty Result!")
+                                    .setMessage("Current picture is empty, please shutting it again!")
+                                    .setCancelable(true)
+                                    .show();
+                        }
+                    }
+                });
+
+            }
+        }).start();
+    }
+
+    private void copyBitmapFromCamera(Bitmap ARGB8888ImageBitmap) {
+        if (isShutterBitmapCopied || ARGB8888ImageBitmap == null) {
+            return;
+        }
+        if (!ARGB8888ImageBitmap.isRecycled()) {
+            synchronized (this) {
+                shutterBitmap = ARGB8888ImageBitmap.copy(Bitmap.Config.ARGB_8888, true);
+                originShutterBitmap = ARGB8888ImageBitmap.copy(Bitmap.Config.ARGB_8888, true);
+            }
+            SystemClock.sleep(TIME_SLEEP_INTERVAL);
+            isShutterBitmapCopied = true;
+        }
+    }
+
+    @Override
+    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
+        super.onActivityResult(requestCode, resultCode, data);
+        if (requestCode == INTENT_CODE_PICK_IMAGE) {
+            if (resultCode == Activity.RESULT_OK) {
+                cameraPageView.setVisibility(View.GONE);
+                resultPageView.setVisibility(View.VISIBLE);
+                seekbarText.setText(resultNum + "");
+                confidenceSeekbar.setProgress((int) (resultNum * 100));
+                Uri uri = data.getData();
+                String path = getRealPathFromURI(this, uri);
+                picBitmap = decodeBitmap(path, 720, 1280);
+                originPicBitmap = picBitmap.copy(Bitmap.Config.ARGB_8888, true);
+                resultImage.setImageBitmap(picBitmap);
+            }
+        }
+    }
+
+    private void toggleRealtimeStyle() {
+        if (isRealtimeStatusRunning) {
+            isRealtimeStatusRunning = false;
+            realtimeToggleButton.setImageResource(R.drawable.realtime_stop_btn);
+            svPreview.setOnTextureChangedListener(this);
+            tvStatus.setVisibility(View.VISIBLE);
+        } else {
+            isRealtimeStatusRunning = true;
+            realtimeToggleButton.setImageResource(R.drawable.realtime_start_btn);
+            tvStatus.setVisibility(View.GONE);
+            isShutterBitmapCopied = false;
+            svPreview.setOnTextureChangedListener(new CameraSurfaceView.OnTextureChangedListener() {
+                @Override
+                public boolean onTextureChanged(Bitmap ARGB8888ImageBitmap) {
+                    if (TYPE == BTN_SHUTTER) {
+                        copyBitmapFromCamera(ARGB8888ImageBitmap);
+                    }
+                    return false;
+                }
+            });
+        }
+    }
+
+    @Override
+    public boolean onTextureChanged(Bitmap ARGB8888ImageBitmap) {
+        if (TYPE == BTN_SHUTTER) {
+            copyBitmapFromCamera(ARGB8888ImageBitmap);
+            return false;
+        }
+
+        boolean modified = false;
+
+        long tc = System.currentTimeMillis();
+        ClassifyResult result = predictor.predict(ARGB8888ImageBitmap,
+                true, ClassificationSettingsActivity.scoreThreshold);
+        timeElapsed += (System.currentTimeMillis() - tc);
+
+        modified = result.initialized();
+
+        frameCounter++;
+        if (frameCounter >= 30) {
+            final int fps = (int) (1000 / (timeElapsed / 30));
+            runOnUiThread(new Runnable() {
+                @SuppressLint("SetTextI18n")
+                public void run() {
+                    tvStatus.setText(Integer.toString(fps) + "fps");
+                }
+            });
+            frameCounter = 0;
+            timeElapsed = 0;
+        }
+        return modified;
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        // Reload settings and re-initialize the predictor
+        checkAndUpdateSettings();
+        // Open camera until the permissions have been granted
+        if (!checkAllPermissions()) {
+            svPreview.disableCamera();
+        } else {
+            svPreview.enableCamera();
+        }
+        svPreview.onResume();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        svPreview.onPause();
+    }
+
+    @Override
+    protected void onDestroy() {
+        if (predictor != null) {
+            predictor.release();
+        }
+        super.onDestroy();
+    }
+
+    public void initView() {
+        TYPE = REALTIME_DETECT;
+        svPreview = (CameraSurfaceView) findViewById(R.id.sv_preview);
+        svPreview.setOnTextureChangedListener(this);
+        tvStatus = (TextView) findViewById(R.id.tv_status);
+        btnSwitch = (ImageButton) findViewById(R.id.btn_switch);
+        btnSwitch.setOnClickListener(this);
+        btnShutter = (ImageButton) findViewById(R.id.btn_shutter);
+        btnShutter.setOnClickListener(this);
+        btnSettings = (ImageButton) findViewById(R.id.btn_settings);
+        btnSettings.setOnClickListener(this);
+        realtimeToggleButton = findViewById(R.id.realtime_toggle_btn);
+        realtimeToggleButton.setOnClickListener(this);
+        backInPreview = findViewById(R.id.back_in_preview);
+        backInPreview.setOnClickListener(this);
+        albumSelectButton = findViewById(R.id.iv_select);
+        albumSelectButton.setOnClickListener(this);
+        cameraPageView = findViewById(R.id.camera_page);
+        resultPageView = findViewById(R.id.result_page);
+        resultImage = findViewById(R.id.result_image);
+        backInResult = findViewById(R.id.back_in_result);
+        backInResult.setOnClickListener(this);
+        confidenceSeekbar = findViewById(R.id.confidence_seekbar);
+        seekbarText = findViewById(R.id.seekbar_text);
+        resultView = findViewById(R.id.result_list_view);
+
+        confidenceSeekbar.setMax(100);
+        confidenceSeekbar.setOnSeekBarChangeListener(new SeekBar.OnSeekBarChangeListener() {
+            @Override
+            public void onProgressChanged(SeekBar seekBar, int progress, boolean fromUser) {
+                float resultConfidence = seekBar.getProgress() / 100f;
+                BigDecimal bd = new BigDecimal(resultConfidence);
+                resultNum = bd.setScale(1, BigDecimal.ROUND_HALF_UP).floatValue();
+                seekbarText.setText(resultNum + "");
+                confidenceSeekbar.setProgress((int) (resultNum * 100));
+                results.clear();
+            }
+
+            @Override
+            public void onStartTrackingTouch(SeekBar seekBar) {
+
+            }
+
+            @Override
+            public void onStopTrackingTouch(SeekBar seekBar) {
+                runOnUiThread(new Runnable() {
+                    @Override
+                    public void run() {
+                        if (TYPE == ALBUM_SELECT) {
+                            SystemClock.sleep(500);
+                            detail(picBitmap);
+                            picBitmap = originPicBitmap.copy(Bitmap.Config.ARGB_8888, true);
+                        } else {
+                            SystemClock.sleep(500);
+                            detail(shutterBitmap);
+                            shutterBitmap = originShutterBitmap.copy(Bitmap.Config.ARGB_8888, true);
+                        }
+                    }
+                });
+            }
+        });
+    }
+
+    private void detail(Bitmap bitmap) {
+
+        ClassifyResult result = predictor.predict(bitmap, true, resultNum);
+
+        scores = result.mScores;
+        labelId = result.mLabelIds;
+
+        initialized = result.initialized();
+        if (initialized) {
+            for (int i = 0; i < labelId.length; i++) {
+                if (scores[i] > resultNum) {
+                    int idx = labelId[i];
+                    String text = labelText.get(idx);
+                    text = text.substring(text.indexOf(" "));
+                    results.add(new BaseResultModel(idx, text, scores[i]));
+                }
+            }
+        }
+        BaseResultAdapter adapter = new BaseResultAdapter(getBaseContext(), R.layout.classification_result_page_item, results);
+        resultView.setAdapter(adapter);
+        resultView.invalidate();
+
+        resultImage.setImageBitmap(bitmap);
+        resultNum = 1.0f;
+    }
+
+    @SuppressLint("ApplySharedPref")
+    public void initSettings() {
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        SharedPreferences.Editor editor = sharedPreferences.edit();
+        editor.clear();
+        editor.commit();
+        ClassificationSettingsActivity.resetSettings();
+    }
+
+    public void checkAndUpdateSettings() {
+        if (ClassificationSettingsActivity.checkAndUpdateSettings(this)) {
+            String realModelDir = getCacheDir() + "/" + ClassificationSettingsActivity.modelDir;
+            Utils.copyDirectoryFromAssets(this, ClassificationSettingsActivity.modelDir, realModelDir);
+            String realLabelPath = getCacheDir() + "/" + ClassificationSettingsActivity.labelPath;
+            Utils.copyFileFromAssets(this, ClassificationSettingsActivity.labelPath, realLabelPath);
+
+            String modelFile = realModelDir + "/" + "inference.pdmodel";
+            String paramsFile = realModelDir + "/" + "inference.pdiparams";
+            String configFile = realModelDir + "/" + "inference_cls.yaml";
+            String labelFile = realLabelPath;
+            labelText = readTxt(labelFile);
+            RuntimeOption option = new RuntimeOption();
+            option.setCpuThreadNum(ClassificationSettingsActivity.cpuThreadNum);
+            option.setLitePowerMode(ClassificationSettingsActivity.cpuPowerMode);
+            if (Boolean.parseBoolean(ClassificationSettingsActivity.enableLiteFp16)) {
+                option.enableLiteFp16();
+            }
+            predictor.init(modelFile, paramsFile, configFile, labelFile, option);
+        }
+    }
+
+    @Override
+    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
+                                           @NonNull int[] grantResults) {
+        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
+        if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
+            new AlertDialog.Builder(ClassificationMainActivity.this)
+                    .setTitle("Permission denied")
+                    .setMessage("Click to force quit the app, then open Settings->Apps & notifications->Target " +
+                            "App->Permissions to grant all of the permissions.")
+                    .setCancelable(false)
+                    .setPositiveButton("Exit", new DialogInterface.OnClickListener() {
+                        @Override
+                        public void onClick(DialogInterface dialog, int which) {
+                            ClassificationMainActivity.this.finish();
+                        }
+                    }).show();
+        }
+    }
+
+    private void requestAllPermissions() {
+        ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
+                Manifest.permission.CAMERA}, 0);
+    }
+
+    private boolean checkAllPermissions() {
+        return ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) == PackageManager.PERMISSION_GRANTED
+                && ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) == PackageManager.PERMISSION_GRANTED;
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/classification/ClassificationSettingsActivity.java

+package com.baidu.paddle.fastdeploy.app.examples.classification;
+
+import android.annotation.SuppressLint;
+import android.content.Context;
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.EditTextPreference;
+import android.preference.ListPreference;
+import android.preference.PreferenceManager;
+import android.support.v7.app.ActionBar;
+
+import com.baidu.paddle.fastdeploy.app.examples.R;
+import com.baidu.paddle.fastdeploy.app.ui.Utils;
+import com.baidu.paddle.fastdeploy.app.ui.view.AppCompatPreferenceActivity;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class ClassificationSettingsActivity extends AppCompatPreferenceActivity implements
+        SharedPreferences.OnSharedPreferenceChangeListener {
+    private static final String TAG = ClassificationSettingsActivity.class.getSimpleName();
+
+    static public int selectedModelIdx = -1;
+    static public String modelDir = "";
+    static public String labelPath = "";
+    static public int cpuThreadNum = 2;
+    static public String cpuPowerMode = "";
+    static public float scoreThreshold = 0.1f;
+    static public String enableLiteFp16 = "true";
+
+    ListPreference lpChoosePreInstalledModel = null;
+    EditTextPreference etModelDir = null;
+    EditTextPreference etLabelPath = null;
+    ListPreference lpCPUThreadNum = null;
+    ListPreference lpCPUPowerMode = null;
+    EditTextPreference etScoreThreshold = null;
+    ListPreference lpEnableLiteFp16 = null;
+
+    List<String> preInstalledModelDirs = null;
+    List<String> preInstalledLabelPaths = null;
+    List<String> preInstalledCPUThreadNums = null;
+    List<String> preInstalledCPUPowerModes = null;
+    List<String> preInstalledScoreThresholds = null;
+    List<String> preInstalledEnableLiteFp16s = null;
+
+    @Override
+    public void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        addPreferencesFromResource(R.xml.classification_settings);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        // Initialize pre-installed models
+        preInstalledModelDirs = new ArrayList<String>();
+        preInstalledLabelPaths = new ArrayList<String>();
+        preInstalledCPUThreadNums = new ArrayList<String>();
+        preInstalledCPUPowerModes = new ArrayList<String>();
+        preInstalledScoreThresholds = new ArrayList<String>();
+        preInstalledEnableLiteFp16s = new ArrayList<String>();
+        preInstalledModelDirs.add(getString(R.string.CLASSIFICATION_MODEL_DIR_DEFAULT));
+        preInstalledLabelPaths.add(getString(R.string.CLASSIFICATION_LABEL_PATH_DEFAULT));
+        preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
+        preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_CLASSIFICATION));
+        preInstalledEnableLiteFp16s.add(getString(R.string.ENABLE_LITE_FP16_MODE_DEFAULT));
+
+        // Setup UI components
+        lpChoosePreInstalledModel =
+                (ListPreference) findPreference(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY));
+        String[] preInstalledModelNames = new String[preInstalledModelDirs.size()];
+        for (int i = 0; i < preInstalledModelDirs.size(); i++) {
+            preInstalledModelNames[i] = preInstalledModelDirs.get(i).substring(preInstalledModelDirs.get(i).lastIndexOf("/") + 1);
+        }
+        lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
+        lpChoosePreInstalledModel.setEntryValues(preInstalledModelDirs.toArray(new String[preInstalledModelDirs.size()]));
+        lpCPUThreadNum = (ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
+        lpCPUPowerMode = (ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
+        etModelDir = (EditTextPreference) findPreference(getString(R.string.MODEL_DIR_KEY));
+        etModelDir.setTitle("Model dir (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etLabelPath = (EditTextPreference) findPreference(getString(R.string.LABEL_PATH_KEY));
+        etLabelPath.setTitle("Label path (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
+        lpEnableLiteFp16 = (ListPreference) findPreference(getString(R.string.ENABLE_LITE_FP16_MODE_KEY));
+    }
+
+    @SuppressLint("ApplySharedPref")
+    private void reloadSettingsAndUpdateUI() {
+        SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
+
+        String selected_model_dir = sharedPreferences.getString(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY),
+                getString(R.string.CLASSIFICATION_MODEL_DIR_DEFAULT));
+        int selected_model_idx = lpChoosePreInstalledModel.findIndexOfValue(selected_model_dir);
+        if (selected_model_idx >= 0 && selected_model_idx < preInstalledModelDirs.size() && selected_model_idx != selectedModelIdx) {
+            SharedPreferences.Editor editor = sharedPreferences.edit();
+            editor.putString(getString(R.string.MODEL_DIR_KEY), preInstalledModelDirs.get(selected_model_idx));
+            editor.putString(getString(R.string.LABEL_PATH_KEY), preInstalledLabelPaths.get(selected_model_idx));
+            editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(selected_model_idx));
+            editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(selected_model_idx));
+            editor.putString(getString(R.string.SCORE_THRESHOLD_KEY), preInstalledScoreThresholds.get(selected_model_idx));
+            editor.putString(getString(R.string.ENABLE_LITE_FP16_MODE_DEFAULT), preInstalledEnableLiteFp16s.get(selected_model_idx));
+            editor.commit();
+            lpChoosePreInstalledModel.setSummary(selected_model_dir);
+            selectedModelIdx = selected_model_idx;
+        }
+
+        String model_dir = sharedPreferences.getString(getString(R.string.MODEL_DIR_KEY),
+                getString(R.string.CLASSIFICATION_MODEL_DIR_DEFAULT));
+        String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
+                getString(R.string.CLASSIFICATION_LABEL_PATH_DEFAULT));
+        String cpu_thread_num = sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
+                getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        String cpu_power_mode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
+                getString(R.string.CPU_POWER_MODE_DEFAULT));
+        String score_threshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
+                getString(R.string.SCORE_THRESHOLD_CLASSIFICATION));
+        String enable_lite_fp16 = sharedPreferences.getString(getString(R.string.ENABLE_LITE_FP16_MODE_KEY),
+                getString(R.string.ENABLE_LITE_FP16_MODE_DEFAULT));
+
+        etModelDir.setSummary(model_dir);
+        etLabelPath.setSummary(label_path);
+        lpCPUThreadNum.setValue(cpu_thread_num);
+        lpCPUThreadNum.setSummary(cpu_thread_num);
+        lpCPUPowerMode.setValue(cpu_power_mode);
+        lpCPUPowerMode.setSummary(cpu_power_mode);
+        etScoreThreshold.setSummary(score_threshold);
+        etScoreThreshold.setText(score_threshold);
+        lpEnableLiteFp16.setValue(enable_lite_fp16);
+        lpEnableLiteFp16.setSummary(enable_lite_fp16);
+
+    }
+
+    static boolean checkAndUpdateSettings(Context ctx) {
+        boolean settingsChanged = false;
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(ctx);
+
+        String model_dir = sharedPreferences.getString(ctx.getString(R.string.MODEL_DIR_KEY),
+                ctx.getString(R.string.CLASSIFICATION_MODEL_DIR_DEFAULT));
+        settingsChanged |= !modelDir.equalsIgnoreCase(model_dir);
+        modelDir = model_dir;
+
+        String label_path = sharedPreferences.getString(ctx.getString(R.string.LABEL_PATH_KEY),
+                ctx.getString(R.string.CLASSIFICATION_LABEL_PATH_DEFAULT));
+        settingsChanged |= !labelPath.equalsIgnoreCase(label_path);
+        labelPath = label_path;
+
+        String cpu_thread_num = sharedPreferences.getString(ctx.getString(R.string.CPU_THREAD_NUM_KEY),
+                ctx.getString(R.string.CPU_THREAD_NUM_DEFAULT));
+        settingsChanged |= cpuThreadNum != Integer.parseInt(cpu_thread_num);
+        cpuThreadNum = Integer.parseInt(cpu_thread_num);
+
+        String cpu_power_mode = sharedPreferences.getString(ctx.getString(R.string.CPU_POWER_MODE_KEY),
+                ctx.getString(R.string.CPU_POWER_MODE_DEFAULT));
+        settingsChanged |= !cpuPowerMode.equalsIgnoreCase(cpu_power_mode);
+        cpuPowerMode = cpu_power_mode;
+
+        String score_threshold = sharedPreferences.getString(ctx.getString(R.string.SCORE_THRESHOLD_KEY),
+                ctx.getString(R.string.SCORE_THRESHOLD_CLASSIFICATION));
+        settingsChanged |= scoreThreshold != Float.parseFloat(score_threshold);
+        scoreThreshold = Float.parseFloat(score_threshold);
+
+        String enable_lite_fp16 = sharedPreferences.getString(ctx.getString(R.string.ENABLE_LITE_FP16_MODE_KEY),
+                ctx.getString(R.string.ENABLE_LITE_FP16_MODE_DEFAULT));
+        settingsChanged |= !enableLiteFp16.equalsIgnoreCase(enable_lite_fp16);
+        enableLiteFp16 = enable_lite_fp16;
+
+        return settingsChanged;
+    }
+
+    static void resetSettings() {
+        selectedModelIdx = -1;
+        modelDir = "";
+        labelPath = "";
+        cpuThreadNum = 2;
+        cpuPowerMode = "";
+        scoreThreshold = 0.1f;
+        enableLiteFp16 = "true";
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
+        reloadSettingsAndUpdateUI();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
+    }
+
+    @Override
+    public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
+        reloadSettingsAndUpdateUI();
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/Utils.java

+package com.baidu.paddle.fastdeploy.app.ui;
+
+import android.content.Context;
+import android.content.res.Resources;
+import android.database.Cursor;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.hardware.Camera;
+import android.net.Uri;
+import android.opengl.GLES20;
+import android.os.Environment;
+import android.provider.MediaStore;
+import android.util.Log;
+import android.view.Surface;
+import android.view.WindowManager;
+
+import java.io.BufferedInputStream;
+import java.io.BufferedOutputStream;
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.io.OutputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class Utils {
+    private static final String TAG = Utils.class.getSimpleName();
+
+    public static void RecursiveCreateDirectories(String fileDir) {
+        String[] fileDirs = fileDir.split("\\/");
+        String topPath = "";
+        for (int i = 0; i < fileDirs.length; i++) {
+            topPath += "/" + fileDirs[i];
+            File file = new File(topPath);
+            if (file.exists()) {
+                continue;
+            } else {
+                file.mkdir();
+            }
+        }
+    }
+
+    public static void copyFileFromAssets(Context appCtx, String srcPath, String dstPath) {
+        if (srcPath.isEmpty() || dstPath.isEmpty()) {
+            return;
+        }
+        String dstDir = dstPath.substring(0, dstPath.lastIndexOf('/'));
+        if (dstDir.length() > 0) {
+            RecursiveCreateDirectories(dstDir);
+        }
+        InputStream is = null;
+        OutputStream os = null;
+        try {
+            is = new BufferedInputStream(appCtx.getAssets().open(srcPath));
+            os = new BufferedOutputStream(new FileOutputStream(new File(dstPath)));
+            byte[] buffer = new byte[1024];
+            int length = 0;
+            while ((length = is.read(buffer)) != -1) {
+                os.write(buffer, 0, length);
+            }
+        } catch (FileNotFoundException e) {
+            e.printStackTrace();
+        } catch (IOException e) {
+            e.printStackTrace();
+        } finally {
+            try {
+                os.close();
+                is.close();
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+    }
+
+    public static void copyDirectoryFromAssets(Context appCtx, String srcDir, String dstDir) {
+        if (srcDir.isEmpty() || dstDir.isEmpty()) {
+            return;
+        }
+        try {
+            if (!new File(dstDir).exists()) {
+                new File(dstDir).mkdirs();
+            }
+            for (String fileName : appCtx.getAssets().list(srcDir)) {
+                String srcSubPath = srcDir + File.separator + fileName;
+                String dstSubPath = dstDir + File.separator + fileName;
+                if (new File(srcSubPath).isDirectory()) {
+                    copyDirectoryFromAssets(appCtx, srcSubPath, dstSubPath);
+                } else {
+                    copyFileFromAssets(appCtx, srcSubPath, dstSubPath);
+                }
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public static float[] parseFloatsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        float[] floats = new float[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            floats[i] = Float.parseFloat(pieces[i].trim());
+        }
+        return floats;
+    }
+
+    public static long[] parseLongsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        long[] longs = new long[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            longs[i] = Long.parseLong(pieces[i].trim());
+        }
+        return longs;
+    }
+
+    public static String getSDCardDirectory() {
+        return Environment.getExternalStorageDirectory().getAbsolutePath();
+    }
+
+    public static String getDCIMDirectory() {
+        return Environment.getExternalStoragePublicDirectory(Environment.DIRECTORY_DCIM).getAbsolutePath();
+    }
+
+    public static Camera.Size getOptimalPreviewSize(List<Camera.Size> sizes, int w, int h) {
+        final double ASPECT_TOLERANCE = 0.3;
+        double targetRatio = (double) w / h;
+        if (sizes == null) return null;
+
+        Camera.Size optimalSize = null;
+        double minDiff = Double.MAX_VALUE;
+
+        int targetHeight = h;
+
+        // Try to find an size match aspect ratio and size
+        for (Camera.Size size : sizes) {
+            double ratio = (double) size.width / size.height;
+            if (Math.abs(ratio - targetRatio) > ASPECT_TOLERANCE) continue;
+            if (Math.abs(size.height - targetHeight) < minDiff) {
+                optimalSize = size;
+                minDiff = Math.abs(size.height - targetHeight);
+            }
+        }
+
+        // Cannot find the one match the aspect ratio, ignore the requirement
+        if (optimalSize == null) {
+            minDiff = Double.MAX_VALUE;
+            for (Camera.Size size : sizes) {
+                if (Math.abs(size.height - targetHeight) < minDiff) {
+                    optimalSize = size;
+                    minDiff = Math.abs(size.height - targetHeight);
+                }
+            }
+        }
+        return optimalSize;
+    }
+
+    public static int getScreenWidth() {
+        return Resources.getSystem().getDisplayMetrics().widthPixels;
+    }
+
+    public static int getScreenHeight() {
+        return Resources.getSystem().getDisplayMetrics().heightPixels;
+    }
+
+    public static int getCameraDisplayOrientation(Context context, int cameraId) {
+        Camera.CameraInfo info = new Camera.CameraInfo();
+        Camera.getCameraInfo(cameraId, info);
+        WindowManager wm = (WindowManager) context.getSystemService(Context.WINDOW_SERVICE);
+        int rotation = wm.getDefaultDisplay().getRotation();
+        int degrees = 0;
+        switch (rotation) {
+            case Surface.ROTATION_0:
+                degrees = 0;
+                break;
+            case Surface.ROTATION_90:
+                degrees = 90;
+                break;
+            case Surface.ROTATION_180:
+                degrees = 180;
+                break;
+            case Surface.ROTATION_270:
+                degrees = 270;
+                break;
+        }
+        int result;
+        if (info.facing == Camera.CameraInfo.CAMERA_FACING_FRONT) {
+            result = (info.orientation + degrees) % 360;
+            result = (360 - result) % 360;   // compensate the mirror
+        } else {
+            // back-facing
+            result = (info.orientation - degrees + 360) % 360;
+        }
+        return result;
+    }
+
+    public static int createShaderProgram(String vss, String fss) {
+        int vshader = GLES20.glCreateShader(GLES20.GL_VERTEX_SHADER);
+        GLES20.glShaderSource(vshader, vss);
+        GLES20.glCompileShader(vshader);
+        int[] status = new int[1];
+        GLES20.glGetShaderiv(vshader, GLES20.GL_COMPILE_STATUS, status, 0);
+        if (status[0] == 0) {
+            Log.e(TAG, GLES20.glGetShaderInfoLog(vshader));
+            GLES20.glDeleteShader(vshader);
+            vshader = 0;
+            return 0;
+        }
+
+        int fshader = GLES20.glCreateShader(GLES20.GL_FRAGMENT_SHADER);
+        GLES20.glShaderSource(fshader, fss);
+        GLES20.glCompileShader(fshader);
+        GLES20.glGetShaderiv(fshader, GLES20.GL_COMPILE_STATUS, status, 0);
+        if (status[0] == 0) {
+            Log.e(TAG, GLES20.glGetShaderInfoLog(fshader));
+            GLES20.glDeleteShader(vshader);
+            GLES20.glDeleteShader(fshader);
+            fshader = 0;
+            return 0;
+        }
+
+        int program = GLES20.glCreateProgram();
+        GLES20.glAttachShader(program, vshader);
+        GLES20.glAttachShader(program, fshader);
+        GLES20.glLinkProgram(program);
+        GLES20.glDeleteShader(vshader);
+        GLES20.glDeleteShader(fshader);
+        GLES20.glGetProgramiv(program, GLES20.GL_LINK_STATUS, status, 0);
+        if (status[0] == 0) {
+            Log.e(TAG, GLES20.glGetProgramInfoLog(program));
+            program = 0;
+            return 0;
+        }
+        GLES20.glValidateProgram(program);
+        GLES20.glGetProgramiv(program, GLES20.GL_VALIDATE_STATUS, status, 0);
+        if (status[0] == 0) {
+            Log.e(TAG, GLES20.glGetProgramInfoLog(program));
+            GLES20.glDeleteProgram(program);
+            program = 0;
+            return 0;
+        }
+
+        return program;
+    }
+
+    public static boolean isSupportedNPU() {
+        String hardware = android.os.Build.HARDWARE;
+        return hardware.equalsIgnoreCase("kirin810") || hardware.equalsIgnoreCase("kirin990");
+    }
+
+    public static Bitmap decodeBitmap(String path, int displayWidth, int displayHeight) {
+        BitmapFactory.Options op = new BitmapFactory.Options();
+        op.inJustDecodeBounds = true;// Only the width and height information of Bitmap is read, not the pixels.
+        Bitmap bmp = BitmapFactory.decodeFile(path, op); // Get size information.
+        int wRatio = (int) Math.ceil(op.outWidth / (float) displayWidth);// Get Scale Size.
+        int hRatio = (int) Math.ceil(op.outHeight / (float) displayHeight);
+        // If the specified size is exceeded, reduce the corresponding scale.
+        if (wRatio > 1 && hRatio > 1) {
+            if (wRatio > hRatio) {
+                // If it is too wide, we will reduce the width to the required size. Note that the height will become smaller.
+                op.inSampleSize = wRatio;
+            } else {
+                op.inSampleSize = hRatio;
+            }
+        }
+        op.inJustDecodeBounds = false;
+        bmp = BitmapFactory.decodeFile(path, op);
+        // Create a Bitmap with a given width and height from the original Bitmap.
+        return Bitmap.createScaledBitmap(bmp, displayWidth, displayHeight, true);
+    }
+
+    public static String getRealPathFromURI(Context context, Uri contentURI) {
+        String result;
+        Cursor cursor = null;
+        try {
+            cursor = context.getContentResolver().query(contentURI, null, null, null, null);
+        } catch (Throwable e) {
+            e.printStackTrace();
+        }
+        if (cursor == null) {
+            result = contentURI.getPath();
+        } else {
+            cursor.moveToFirst();
+            int idx = cursor.getColumnIndex(MediaStore.Images.ImageColumns.DATA);
+            result = cursor.getString(idx);
+            cursor.close();
+        }
+        return result;
+    }
+
+    public static List<String> readTxt(String txtPath) {
+        File file = new File(txtPath);
+        if (file.isFile() && file.exists()) {
+            try {
+                FileInputStream fileInputStream = new FileInputStream(file);
+                InputStreamReader inputStreamReader = new InputStreamReader(fileInputStream);
+                BufferedReader bufferedReader = new BufferedReader(inputStreamReader);
+                String text;
+                List<String> labels = new ArrayList<>();
+                while ((text = bufferedReader.readLine()) != null) {
+                    labels.add(text);
+                }
+                return labels;
+            } catch (Exception e) {
+                e.printStackTrace();
+            }
+        }
+        return null;
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/layout/ActionBarLayout.java

+package com.baidu.paddle.fastdeploy.app.ui.layout;
+
+import android.content.Context;
+import android.graphics.Color;
+import android.support.annotation.Nullable;
+import android.util.AttributeSet;
+import android.widget.RelativeLayout;
+
+
+public class ActionBarLayout extends RelativeLayout {
+    private int layoutHeight = 150;
+
+    public ActionBarLayout(Context context) {
+        super(context);
+    }
+
+    public ActionBarLayout(Context context, @Nullable AttributeSet attrs) {
+        super(context, attrs);
+    }
+
+    public ActionBarLayout(Context context, @Nullable AttributeSet attrs, int defStyleAttr) {
+        super(context, attrs, defStyleAttr);
+    }
+
+    @Override
+    protected void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
+        super.onMeasure(widthMeasureSpec, heightMeasureSpec);
+        int width = MeasureSpec.getSize(widthMeasureSpec);
+        setMeasuredDimension(width, layoutHeight);
+        setBackgroundColor(Color.BLACK);
+        setAlpha(0.9f);
+    }
+}
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/view/AppCompatPreferenceActivity.java

+package com.baidu.paddle.fastdeploy.app.ui.view;
+
+import android.content.res.Configuration;
+import android.os.Bundle;
+import android.preference.PreferenceActivity;
+import android.support.annotation.LayoutRes;
+import android.support.annotation.Nullable;
+import android.support.v7.app.ActionBar;
+import android.support.v7.app.AppCompatDelegate;
+import android.support.v7.widget.Toolbar;
+import android.view.MenuInflater;
+import android.view.View;
+import android.view.ViewGroup;
+
+/**
+ * A {@link PreferenceActivity} which implements and proxies the necessary calls
+ * to be used with AppCompat.
+ * <p>
+ * This technique can be used with an {@link android.app.Activity} class, not just
+ * {@link PreferenceActivity}.
+ */
+public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
+    private AppCompatDelegate mDelegate;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        getDelegate().installViewFactory();
+        getDelegate().onCreate(savedInstanceState);
+        super.onCreate(savedInstanceState);
+    }
+
+    @Override
+    protected void onPostCreate(Bundle savedInstanceState) {
+        super.onPostCreate(savedInstanceState);
+        getDelegate().onPostCreate(savedInstanceState);
+    }
+
+    public ActionBar getSupportActionBar() {
+        return getDelegate().getSupportActionBar();
+    }
+
+    public void setSupportActionBar(@Nullable Toolbar toolbar) {
+        getDelegate().setSupportActionBar(toolbar);
+    }
+
+    @Override
+    public MenuInflater getMenuInflater() {
+        return getDelegate().getMenuInflater();
+    }
+
+    @Override
+    public void setContentView(@LayoutRes int layoutResID) {
+        getDelegate().setContentView(layoutResID);
+    }
+
+    @Override
+    public void setContentView(View view) {
+        getDelegate().setContentView(view);
+    }
+
+    @Override
+    public void setContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().setContentView(view, params);
+    }
+
+    @Override
+    public void addContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().addContentView(view, params);
+    }
+
+    @Override
+    protected void onPostResume() {
+        super.onPostResume();
+        getDelegate().onPostResume();
+    }
+
+    @Override
+    protected void onTitleChanged(CharSequence title, int color) {
+        super.onTitleChanged(title, color);
+        getDelegate().setTitle(title);
+    }
+
+    @Override
+    public void onConfigurationChanged(Configuration newConfig) {
+        super.onConfigurationChanged(newConfig);
+        getDelegate().onConfigurationChanged(newConfig);
+    }
+
+    @Override
+    protected void onStop() {
+        super.onStop();
+        getDelegate().onStop();
+    }
+
+    @Override
+    protected void onDestroy() {
+        super.onDestroy();
+        getDelegate().onDestroy();
+    }
+
+    public void invalidateOptionsMenu() {
+        getDelegate().invalidateOptionsMenu();
+    }
+
+    private AppCompatDelegate getDelegate() {
+        if (mDelegate == null) {
+            mDelegate = AppCompatDelegate.create(this, null);
+        }
+        return mDelegate;
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/view/CameraSurfaceView.java

+package com.baidu.paddle.fastdeploy.app.ui.view;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.SurfaceTexture;
+import android.hardware.Camera;
+import android.hardware.Camera.CameraInfo;
+import android.hardware.Camera.Size;
+import android.opengl.GLES11Ext;
+import android.opengl.GLES20;
+import android.opengl.GLSurfaceView;
+import android.opengl.GLSurfaceView.Renderer;
+import android.opengl.GLUtils;
+import android.opengl.Matrix;
+import android.util.AttributeSet;
+import android.util.Log;
+
+import com.baidu.paddle.fastdeploy.app.ui.Utils;
+
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.FloatBuffer;
+import java.util.List;
+
+import javax.microedition.khronos.egl.EGLConfig;
+import javax.microedition.khronos.opengles.GL10;
+
+public class CameraSurfaceView extends GLSurfaceView implements Renderer,
+        SurfaceTexture.OnFrameAvailableListener {
+    private static final String TAG = CameraSurfaceView.class.getSimpleName();
+
+    public static int EXPECTED_PREVIEW_WIDTH = 1280;  // 1920
+    public static int EXPECTED_PREVIEW_HEIGHT = 720;  // 960
+
+    protected int numberOfCameras;
+    protected int selectedCameraId;
+    protected boolean disableCamera = false;
+    protected Camera camera;
+
+    protected Context context;
+    protected SurfaceTexture surfaceTexture;
+    protected int surfaceWidth = 0;
+    protected int surfaceHeight = 0;
+    protected int textureWidth = 0;
+    protected int textureHeight = 0;
+
+    protected Bitmap ARGB8888ImageBitmap;
+    protected boolean bitmapReleaseMode = true;
+
+    // In order to manipulate the camera preview data and render the modified one
+    // to the screen, three textures are created and the data flow is shown as following:
+    // previewdata->camTextureId->fboTexureId->drawTexureId->framebuffer
+    protected int[] fbo = {0};
+    protected int[] camTextureId = {0};
+    protected int[] fboTexureId = {0};
+    protected int[] drawTexureId = {0};
+
+    private final String vss = ""
+            + "attribute vec2 vPosition;\n"
+            + "attribute vec2 vTexCoord;\n" + "varying vec2 texCoord;\n"
+            + "void main() {\n" + "  texCoord = vTexCoord;\n"
+            + "  gl_Position = vec4 (vPosition.x, vPosition.y, 0.0, 1.0);\n"
+            + "}";
+
+    private final String fssCam2FBO = ""
+            + "#extension GL_OES_EGL_image_external : require\n"
+            + "precision mediump float;\n"
+            + "uniform samplerExternalOES sTexture;\n"
+            + "varying vec2 texCoord;\n"
+            + "void main() {\n"
+            + "  gl_FragColor = texture2D(sTexture,texCoord);\n" + "}";
+
+    private final String fssTex2Screen = ""
+            + "precision mediump float;\n"
+            + "uniform sampler2D sTexture;\n"
+            + "varying vec2 texCoord;\n"
+            + "void main() {\n"
+            + "  gl_FragColor = texture2D(sTexture,texCoord);\n" + "}";
+
+    private final float[] vertexCoords = {
+            -1, -1,
+            -1, 1,
+            1, -1,
+            1, 1};
+    private float[] textureCoords = {
+            0, 1,
+            0, 0,
+            1, 1,
+            1, 0};
+
+    private FloatBuffer vertexCoordsBuffer;
+    private FloatBuffer textureCoordsBuffer;
+
+    private int progCam2FBO = -1;
+    private int progTex2Screen = -1;
+    private int vcCam2FBO;
+    private int tcCam2FBO;
+    private int vcTex2Screen;
+    private int tcTex2Screen;
+
+    public void setBitmapReleaseMode(boolean mode) {
+        synchronized (this) {
+            bitmapReleaseMode = mode;
+        }
+    }
+
+    public Bitmap getBitmap() {
+        return ARGB8888ImageBitmap; // may null or recycled.
+    }
+
+    public interface OnTextureChangedListener {
+        boolean onTextureChanged(Bitmap ARGB8888ImageBitmap);
+    }
+
+    private OnTextureChangedListener onTextureChangedListener = null;
+
+    public void setOnTextureChangedListener(OnTextureChangedListener listener) {
+        onTextureChangedListener = listener;
+    }
+
+    public CameraSurfaceView(Context ctx, AttributeSet attrs) {
+        super(ctx, attrs);
+        context = ctx;
+        setEGLContextClientVersion(2);
+        setRenderer(this);
+        setRenderMode(RENDERMODE_WHEN_DIRTY);
+
+        // Find the total number of available cameras and the ID of the default camera
+        numberOfCameras = Camera.getNumberOfCameras();
+        CameraInfo cameraInfo = new CameraInfo();
+        for (int i = 0; i < numberOfCameras; i++) {
+            Camera.getCameraInfo(i, cameraInfo);
+            if (cameraInfo.facing == CameraInfo.CAMERA_FACING_BACK) {
+                selectedCameraId = i;
+            }
+        }
+    }
+
+    @Override
+    public void onSurfaceCreated(GL10 gl, EGLConfig config) {
+        // Create OES texture for storing camera preview data(YUV format)
+        GLES20.glGenTextures(1, camTextureId, 0);
+        GLES20.glBindTexture(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, camTextureId[0]);
+        GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
+        GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
+        surfaceTexture = new SurfaceTexture(camTextureId[0]);
+        surfaceTexture.setOnFrameAvailableListener(this);
+
+        // Prepare vertex and texture coordinates
+        int bytes = vertexCoords.length * Float.SIZE / Byte.SIZE;
+        vertexCoordsBuffer = ByteBuffer.allocateDirect(bytes).order(ByteOrder.nativeOrder()).asFloatBuffer();
+        textureCoordsBuffer = ByteBuffer.allocateDirect(bytes).order(ByteOrder.nativeOrder()).asFloatBuffer();
+        vertexCoordsBuffer.put(vertexCoords).position(0);
+        textureCoordsBuffer.put(textureCoords).position(0);
+
+        // Create vertex and fragment shaders
+        // camTextureId->fboTexureId
+        progCam2FBO = Utils.createShaderProgram(vss, fssCam2FBO);
+        vcCam2FBO = GLES20.glGetAttribLocation(progCam2FBO, "vPosition");
+        tcCam2FBO = GLES20.glGetAttribLocation(progCam2FBO, "vTexCoord");
+        GLES20.glEnableVertexAttribArray(vcCam2FBO);
+        GLES20.glEnableVertexAttribArray(tcCam2FBO);
+        // fboTexureId/drawTexureId -> screen
+        progTex2Screen = Utils.createShaderProgram(vss, fssTex2Screen);
+        vcTex2Screen = GLES20.glGetAttribLocation(progTex2Screen, "vPosition");
+        tcTex2Screen = GLES20.glGetAttribLocation(progTex2Screen, "vTexCoord");
+        GLES20.glEnableVertexAttribArray(vcTex2Screen);
+        GLES20.glEnableVertexAttribArray(tcTex2Screen);
+    }
+
+    @Override
+    public void onSurfaceChanged(GL10 gl, int width, int height) {
+        surfaceWidth = width;
+        surfaceHeight = height;
+        openCamera();
+    }
+
+    @Override
+    public void onDrawFrame(GL10 gl) {
+        if (surfaceTexture == null) return;
+
+        GLES20.glClearColor(0.0f, 0.0f, 0.0f, 1.0f);
+        GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT | GLES20.GL_DEPTH_BUFFER_BIT);
+        surfaceTexture.updateTexImage();
+        float[] matrix = new float[16];
+        surfaceTexture.getTransformMatrix(matrix);
+
+        // camTextureId->fboTexureId
+        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, fbo[0]);
+        GLES20.glViewport(0, 0, textureWidth, textureHeight);
+        GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT);
+        GLES20.glUseProgram(progCam2FBO);
+        GLES20.glVertexAttribPointer(vcCam2FBO, 2, GLES20.GL_FLOAT, false, 4 * 2, vertexCoordsBuffer);
+        textureCoordsBuffer.clear();
+        textureCoordsBuffer.put(transformTextureCoordinates(textureCoords, matrix));
+        textureCoordsBuffer.position(0);
+        GLES20.glVertexAttribPointer(tcCam2FBO, 2, GLES20.GL_FLOAT, false, 4 * 2, textureCoordsBuffer);
+        GLES20.glActiveTexture(GLES20.GL_TEXTURE0);
+        GLES20.glBindTexture(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, camTextureId[0]);
+        GLES20.glUniform1i(GLES20.glGetUniformLocation(progCam2FBO, "sTexture"), 0);
+        GLES20.glDrawArrays(GLES20.GL_TRIANGLE_STRIP, 0, 4);
+        GLES20.glFlush();
+
+        // Check if the draw texture is set
+        int targetTexureId = fboTexureId[0];
+        if (onTextureChangedListener != null) {
+            // Read pixels of FBO to a bitmap
+            ByteBuffer pixelBuffer = ByteBuffer.allocate(textureWidth * textureHeight * 4);
+            GLES20.glReadPixels(0, 0, textureWidth, textureHeight, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, pixelBuffer);
+
+            ARGB8888ImageBitmap = Bitmap.createBitmap(textureWidth, textureHeight, Bitmap.Config.ARGB_8888);
+            ARGB8888ImageBitmap.copyPixelsFromBuffer(pixelBuffer);
+
+            boolean modified = onTextureChangedListener.onTextureChanged(ARGB8888ImageBitmap);
+
+            if (modified) {
+                targetTexureId = drawTexureId[0];
+                // Update a bitmap to the GL texture if modified
+                GLES20.glActiveTexture(targetTexureId);
+                // GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, targetTexureId);
+                GLES20.glBindTexture(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, targetTexureId);
+                GLUtils.texImage2D(GL10.GL_TEXTURE_2D, 0, ARGB8888ImageBitmap, 0);
+            }
+            if (bitmapReleaseMode) {
+                ARGB8888ImageBitmap.recycle();
+            }
+        }
+
+        // fboTexureId/drawTexureId->Screen
+        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, 0);
+        GLES20.glViewport(0, 0, surfaceWidth, surfaceHeight);
+        GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT);
+        GLES20.glUseProgram(progTex2Screen);
+        GLES20.glVertexAttribPointer(vcTex2Screen, 2, GLES20.GL_FLOAT, false, 4 * 2, vertexCoordsBuffer);
+        textureCoordsBuffer.clear();
+        textureCoordsBuffer.put(textureCoords);
+        textureCoordsBuffer.position(0);
+        GLES20.glVertexAttribPointer(tcTex2Screen, 2, GLES20.GL_FLOAT, false, 4 * 2, textureCoordsBuffer);
+        GLES20.glActiveTexture(GLES20.GL_TEXTURE0);
+        GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, targetTexureId);
+        GLES20.glUniform1i(GLES20.glGetUniformLocation(progTex2Screen, "sTexture"), 0);
+        GLES20.glDrawArrays(GLES20.GL_TRIANGLE_STRIP, 0, 4);
+        GLES20.glFlush();
+    }
+
+    private float[] transformTextureCoordinates(float[] coords, float[] matrix) {
+        float[] result = new float[coords.length];
+        float[] vt = new float[4];
+        for (int i = 0; i < coords.length; i += 2) {
+            float[] v = {coords[i], coords[i + 1], 0, 1};
+            Matrix.multiplyMV(vt, 0, matrix, 0, v, 0);
+            result[i] = vt[0];
+            result[i + 1] = vt[1];
+        }
+        return result;
+    }
+
+    @Override
+    public void onResume() {
+        super.onResume();
+    }
+
+    @Override
+    public void onPause() {
+        super.onPause();
+        releaseCamera();
+    }
+
+    @Override
+    public void onFrameAvailable(SurfaceTexture surfaceTexture) {
+        requestRender();
+    }
+
+    public void disableCamera() {
+        disableCamera = true;
+    }
+
+    public void enableCamera() {
+        disableCamera = false;
+    }
+
+    public void switchCamera() {
+        releaseCamera();
+        selectedCameraId = (selectedCameraId + 1) % numberOfCameras;
+        openCamera();
+    }
+
+    public void openCamera() {
+        if (disableCamera) return;
+        camera = Camera.open(selectedCameraId);
+        List<Size> supportedPreviewSizes = camera.getParameters().getSupportedPreviewSizes();
+        Size previewSize = Utils.getOptimalPreviewSize(supportedPreviewSizes, EXPECTED_PREVIEW_WIDTH,
+                EXPECTED_PREVIEW_HEIGHT);
+        Camera.Parameters parameters = camera.getParameters();
+        parameters.setPreviewSize(previewSize.width, previewSize.height);
+        if (parameters.getSupportedFocusModes().contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO)) {
+            parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO);
+        }
+        camera.setParameters(parameters);
+        int degree = Utils.getCameraDisplayOrientation(context, selectedCameraId);
+        camera.setDisplayOrientation(degree);
+        boolean rotate = degree == 90 || degree == 270;
+        textureWidth = rotate ? previewSize.height : previewSize.width;
+        textureHeight = rotate ? previewSize.width : previewSize.height;
+        // Destroy FBO and draw textures
+        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, 0);
+        GLES20.glDeleteFramebuffers(1, fbo, 0);
+        GLES20.glDeleteTextures(1, drawTexureId, 0);
+        GLES20.glDeleteTextures(1, fboTexureId, 0);
+        // Normal texture for storing modified camera preview data(RGBA format)
+        GLES20.glGenTextures(1, drawTexureId, 0);
+        GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, drawTexureId[0]);
+        GLES20.glTexImage2D(GLES20.GL_TEXTURE_2D, 0, GLES20.GL_RGBA, textureWidth, textureHeight, 0,
+                GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, null);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
+        // FBO texture for storing camera preview data(RGBA format)
+        GLES20.glGenTextures(1, fboTexureId, 0);
+        GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, fboTexureId[0]);
+        GLES20.glTexImage2D(GLES20.GL_TEXTURE_2D, 0, GLES20.GL_RGBA, textureWidth, textureHeight, 0,
+                GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, null);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
+        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
+        // Generate FBO and bind to FBO texture
+        GLES20.glGenFramebuffers(1, fbo, 0);
+        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, fbo[0]);
+        GLES20.glFramebufferTexture2D(GLES20.GL_FRAMEBUFFER, GLES20.GL_COLOR_ATTACHMENT0, GLES20.GL_TEXTURE_2D,
+                fboTexureId[0], 0);
+        try {
+            camera.setPreviewTexture(surfaceTexture);
+        } catch (IOException exception) {
+            Log.e(TAG, "IOException caused by setPreviewDisplay()", exception);
+        }
+        camera.startPreview();
+    }
+
+    public void releaseCamera() {
+        if (camera != null) {
+            camera.setPreviewCallback(null);
+            camera.stopPreview();
+            camera.release();
+            camera = null;
+        }
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/view/ResultListView.java

+package com.baidu.paddle.fastdeploy.app.ui.view;
+
+import android.content.Context;
+import android.os.Handler;
+import android.util.AttributeSet;
+import android.widget.ListView;
+
+public class ResultListView extends ListView {
+    public ResultListView(Context context) {
+        super(context);
+    }
+
+    public ResultListView(Context context, AttributeSet attrs) {
+        super(context, attrs);
+    }
+
+    public ResultListView(Context context, AttributeSet attrs, int defStyleAttr) {
+        super(context, attrs, defStyleAttr);
+    }
+
+    private Handler handler;
+
+    public void setHandler(Handler mHandler) {
+        handler = mHandler;
+    }
+
+    public void clear() {
+        handler.post(new Runnable() {
+            @Override
+            public void run() {
+                removeAllViewsInLayout();
+                invalidate();
+            }
+        });
+    }
+
+    @Override
+    protected void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
+        int expandSpec = MeasureSpec.makeMeasureSpec(Integer.MAX_VALUE >> 2,
+                MeasureSpec.AT_MOST);
+        super.onMeasure(widthMeasureSpec, expandSpec);
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/view/adapter/BaseResultAdapter.java

+package com.baidu.paddle.fastdeploy.app.ui.view.adapter;
+
+import android.content.Context;
+import android.support.annotation.NonNull;
+import android.support.annotation.Nullable;
+import android.view.LayoutInflater;
+import android.view.View;
+import android.view.ViewGroup;
+import android.widget.ArrayAdapter;
+import android.widget.TextView;
+
+import com.baidu.paddle.fastdeploy.app.examples.R;
+import com.baidu.paddle.fastdeploy.app.ui.view.model.BaseResultModel;
+
+import java.text.DecimalFormat;
+import java.util.List;
+
+public class BaseResultAdapter extends ArrayAdapter<BaseResultModel> {
+    private int resourceId;
+
+    public BaseResultAdapter(@NonNull Context context, int resource) {
+        super(context, resource);
+    }
+
+    public BaseResultAdapter(@NonNull Context context, int resource, @NonNull List<BaseResultModel> objects) {
+        super(context, resource, objects);
+        resourceId = resource;
+    }
+
+    @NonNull
+    @Override
+    public View getView(int position, @Nullable View convertView, @NonNull ViewGroup parent) {
+        BaseResultModel model = getItem(position);
+        View view = LayoutInflater.from(getContext()).inflate(resourceId, null);
+        TextView indexText = (TextView) view.findViewById(R.id.index);
+        TextView nameText = (TextView) view.findViewById(R.id.name);
+        TextView confidenceText = (TextView) view.findViewById(R.id.confidence);
+        indexText.setText(String.valueOf(model.getIndex()));
+        nameText.setText(String.valueOf(model.getName()));
+        confidenceText.setText(formatFloatString(model.getConfidence()));
+        return view;
+    }
+
+    public static String formatFloatString(float number) {
+        DecimalFormat df = new DecimalFormat("0.00");
+        return df.format(number);
+    }
+}

deploy/fastdeploy/android/app/src/main/java/com/baidu/paddle/fastdeploy/app/ui/view/model/BaseResultModel.java

+package com.baidu.paddle.fastdeploy.app.ui.view.model;
+
+public class BaseResultModel {
+    private int index;
+    private String name;
+    private float confidence;
+
+    public BaseResultModel() {
+
+    }
+
+    public BaseResultModel(int index, String name, float confidence) {
+        this.index = index;
+        this.name = name;
+        this.confidence = confidence;
+    }
+
+    public float getConfidence() {
+        return confidence;
+    }
+
+    public void setConfidence(float confidence) {
+        this.confidence = confidence;
+    }
+
+    public int getIndex() {
+        return index;
+    }
+
+    public void setIndex(int index) {
+        this.index = index;
+    }
+
+    public String getName() {
+        return name;
+    }
+
+    public void setName(String name) {
+        this.name = name;
+    }
+}

deploy/fastdeploy/android/app/src/main/res/drawable-v24/action_button_layer.xml

+<?xml version="1.0" encoding="utf-8"?>
+<layer-list xmlns:android="http://schemas.android.com/apk/res/android">
+    <item>
+        <shape>
+            <solid android:color="@color/textColorHighlight" />
+        </shape>
+    </item>
+    <!--这里的right和bottom表示的是这一层图片的右边距和下边距，当然还有left和top-->
+    <item android:bottom="5px">
+        <shape>
+            <solid android:color="@color/bk_black" />
+        </shape>
+    </item>
+</layer-list>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/album_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_selected="true" android:drawable="@drawable/album" />
+    <item android:state_focused="true" android:drawable="@drawable/album" />
+    <item android:state_pressed="true" android:drawable="@drawable/album" />
+    <item android:drawable="@drawable/album_pressed" />
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml

+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillType="evenOdd"
+        android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="78.5885"
+                android:endY="90.9159"
+                android:startX="48.7653"
+                android:startY="61.0927"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>

deploy/fastdeploy/android/app/src/main/res/drawable-v24/realtime_start_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_selected="true" android:drawable="@drawable/realtime_start_pressed" />
+    <item android:state_focused="true" android:drawable="@drawable/realtime_start_pressed" />
+    <item android:state_pressed="true" android:drawable="@drawable/realtime_start_pressed" />
+    <item android:drawable="@drawable/realtime_start" />
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/realtime_stop_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_selected="true" android:drawable="@drawable/realtime_stop_pressed" />
+    <item android:state_focused="true" android:drawable="@drawable/realtime_stop_pressed" />
+    <item android:state_pressed="true" android:drawable="@drawable/realtime_stop_pressed" />
+    <item android:drawable="@drawable/realtime_stop" />
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/result_page_border_section_bk.xml

+<?xml version="1.0" encoding="utf-8"?>
+<layer-list xmlns:android="http://schemas.android.com/apk/res/android">
+    <item>
+        <shape android:shape="rectangle">
+            <solid android:color="#FFFFFF" />
+
+            <stroke
+                android:width="1px"
+                android:color="#E5E5E5" />
+        </shape>
+    </item>
+</layer-list>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/round_corner_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<shape xmlns:android="http://schemas.android.com/apk/res/android"
+    android:shape="rectangle">
+    <corners
+        android:bottomLeftRadius="25dp"
+        android:bottomRightRadius="25dp"
+        android:topLeftRadius="25dp"
+        android:topRightRadius="25dp"></corners>
+    <solid android:color="#3B85F5"></solid>
+</shape>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/seekbar_progress_realtime.xml

+<?xml version="1.0" encoding="utf-8"?>
+<layer-list xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <!--定义seekbar滑动条的底色-->
+    <item android:id="@android:id/background">
+        <bitmap
+            android:src="@drawable/seekbar_progress_dotted"
+            android:tileMode="mirror"></bitmap>
+    </item>
+    <!--定义seekbar滑动条进度颜色-->
+    <item android:id="@android:id/progress">
+        <clip>
+            <shape>
+                <solid android:color="#F5A623" />
+            </shape>
+        </clip>
+    </item>
+</layer-list>

deploy/fastdeploy/android/app/src/main/res/drawable-v24/seekbar_progress_result.xml

+<?xml version="1.0" encoding="utf-8"?>
+<layer-list xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <!--定义seekbar滑动条的底色-->
+    <item android:id="@android:id/background">
+        <shape>
+            <solid android:color="#E5E5E5" />
+        </shape>
+    </item>
+    <!--定义seekbar滑动条进度颜色-->
+    <item android:id="@android:id/progress">
+        <clip>
+            <shape>
+                <solid android:color="#3B85F5" />
+            </shape>
+        </clip>
+    </item>
+</layer-list>

deploy/fastdeploy/android/app/src/main/res/drawable-v24/seekbar_thumb.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <selector>
+        <item android:state_focused="true" android:state_pressed="false" android:drawable="@drawable/seekbar_thumb_shape" />
+        <item android:state_focused="true" android:state_pressed="true" android:drawable="@drawable/seekbar_thumb_shape" />
+        <item android:state_focused="false" android:state_pressed="true" android:drawable="@drawable/seekbar_thumb_shape" />
+        <item android:drawable="@drawable/seekbar_thumb_shape" />
+    </selector>
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/seekbar_thumb_shape.xml

+<?xml version="1.0" encoding="utf-8"?>
+<layer-list xmlns:android="http://schemas.android.com/apk/res/android">
+    <!-- 白色前景 -->
+    <item
+        android:gravity="center"
+        android:bottom="20px"
+        android:top="20px"
+        android:right="20px"
+        android:left="20px">
+        <shape android:shape="oval">
+            <size
+                android:width="20px"
+                android:height="20px" />
+            <solid android:color="#ffffffff" />
+        </shape>
+    </item>
+    <!-- 透明阴影 -->
+    <item android:gravity="center">
+        <shape android:shape="oval">
+            <size
+                android:height="30px"
+                android:width="30px" />
+            <solid android:color="#96ffffff" />
+        </shape>
+    </item>
+</layer-list>

deploy/fastdeploy/android/app/src/main/res/drawable-v24/switch_side_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_selected="true" android:drawable="@drawable/switch_side_pressed" />
+    <item android:state_focused="true" android:drawable="@drawable/switch_side_pressed" />
+    <item android:state_pressed="true" android:drawable="@drawable/switch_side_pressed" />
+    <item android:drawable="@drawable/switch_side" />
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable-v24/take_picture_btn.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_selected="true" android:drawable="@drawable/take_picture_pressed" />
+    <item android:state_focused="true" android:drawable="@drawable/take_picture_pressed" />
+    <item android:state_pressed="true" android:drawable="@drawable/take_picture_pressed" />
+    <item android:drawable="@drawable/take_picture" />
+</selector>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/drawable/btn_settings.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_pressed="true" android:drawable="@drawable/btn_settings_pressed"/>
+    <item android:drawable="@drawable/btn_settings_default"/>
+</selector>
+

deploy/fastdeploy/android/app/src/main/res/drawable/btn_settings_default.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="24dp"
+    android:height="24dp"
+    android:viewportWidth="24"
+    android:viewportHeight="24">
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="evenOdd"
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+        android:strokeWidth="0"
+        android:strokeColor="#00000000" />
+</vector>

deploy/fastdeploy/android/app/src/main/res/drawable/btn_settings_pressed.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="24dp"
+    android:height="24dp"
+    android:viewportWidth="24"
+    android:viewportHeight="24">
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+</vector>

deploy/fastdeploy/android/app/src/main/res/drawable/btn_shutter.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_pressed="true" android:drawable="@drawable/btn_shutter_pressed"/>
+    <item android:drawable="@drawable/btn_shutter_default"/>
+</selector>

deploy/fastdeploy/android/app/src/main/res/drawable/btn_shutter_default.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+        android:height="201dp"
+        android:width="201dp"
+        android:viewportWidth="201"
+        android:viewportHeight="201">
+    <path
+            android:fillColor="#ffffff"
+            android:pathData="M 100.5 0 C 156.004617358 0 201 44.995382642 201 100.5 C 201 156.004617358 156.004617358 201 100.5 201 C 44.995382642 201 0 156.004617358 0 100.5 C 0 44.995382642 44.995382642 0 100.5 0 Z"
+            android:strokeWidth="1"
+            android:fillType="evenOdd"/>
+    <path
+            android:fillColor="#dfe7eb"
+            android:pathData="M 100.5 17.2285714 C 146.489540112 17.2285714 183.7714286 54.5104598876 183.7714286 100.5 C 183.7714286 146.489540112 146.489540112 183.7714286 100.5 183.7714286 C 54.5104598876 183.7714286 17.2285714 146.489540112 17.2285714 100.5 C 17.2285714 54.5104598876 54.5104598876 17.2285714 100.5 17.2285714 Z"
+            android:strokeWidth="1"
+            android:fillType="evenOdd"/>
+</vector>

deploy/fastdeploy/android/app/src/main/res/drawable/btn_shutter_pressed.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+        android:height="201dp"
+        android:width="201dp"
+        android:viewportWidth="201"
+        android:viewportHeight="201">
+    <path
+            android:fillColor="#ffffff"
+            android:pathData="M 100.5 0 C 156.004617358 0 201 44.995382642 201 100.5 C 201 156.004617358 156.004617358 201 100.5 201 C 44.995382642 201 0 156.004617358 0 100.5 C 0 44.995382642 44.995382642 0 100.5 0 Z"
+            android:strokeWidth="1"
+            android:fillType="evenOdd"/>
+    <path
+            android:fillColor="#cfd7db"
+            android:pathData="M 100.5 17.2285714 C 146.489540112 17.2285714 183.7714286 54.5104598876 183.7714286 100.5 C 183.7714286 146.489540112 146.489540112 183.7714286 100.5 183.7714286 C 54.5104598876 183.7714286 17.2285714 146.489540112 17.2285714 100.5 C 17.2285714 54.5104598876 54.5104598876 17.2285714 100.5 17.2285714 Z"
+            android:strokeWidth="1"
+            android:fillType="evenOdd"/>
+</vector>

deploy/fastdeploy/android/app/src/main/res/drawable/btn_switch.xml

+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+    <item android:state_pressed="true" android:drawable="@drawable/btn_switch_pressed"/>
+    <item android:drawable="@drawable/btn_switch_default"/>
+</selector>

deploy/fastdeploy/android/app/src/main/res/drawable/ic_launcher_background.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#008577"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
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+    <path
+        android:fillColor="#00000000"
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+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>

deploy/fastdeploy/android/app/src/main/res/layout/classification_activity_main.xml

+<?xml version="1.0" encoding="utf-8"?>
+<FrameLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent">
+
+    <include
+        layout="@layout/classification_camera_page"
+        android:id="@+id/camera_page"></include>
+
+    <include
+        layout="@layout/classification_result_page"
+        android:id="@+id/result_page"
+        android:visibility="gone"></include>
+</FrameLayout>

deploy/fastdeploy/android/app/src/main/res/layout/classification_camera_page.xml

+<?xml version="1.0" encoding="utf-8"?>
+<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:keepScreenOn="true"
+    tools:context=".detection.DetectionMainActivity">
+
+    <RelativeLayout
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:background="@color/colorWindow">
+
+        <com.baidu.paddle.fastdeploy.app.ui.layout.ActionBarLayout
+            android:id="@+id/action_bar_main"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content">
+
+            <ImageView
+                android:id="@+id/back_in_preview"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:cropToPadding="true"
+                android:paddingLeft="40px"
+                android:paddingTop="60px"
+                android:paddingRight="60px"
+                android:paddingBottom="40px"
+                android:src="@drawable/back_btn" />
+
+            <LinearLayout
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_centerHorizontal="true"
+                android:layout_marginTop="50px"
+                android:orientation="horizontal">
+
+                <TextView
+                    android:id="@+id/action_takepicture_btn"
+                    style="@style/action_btn_selected"
+                    android:layout_width="300px"
+                    android:layout_height="wrap_content"
+                    android:text="@string/action_bar_take_photo"
+                    android:textAlignment="center"
+                    android:visibility="gone"/>
+
+                <TextView
+                    android:id="@+id/action_realtime_btn"
+                    style="@style/action_btn"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:text="@string/action_bar_realtime"
+                    android:textAlignment="center"
+                    android:textSize="15sp"/>
+            </LinearLayout>
+        </com.baidu.paddle.fastdeploy.app.ui.layout.ActionBarLayout>
+
+        <com.baidu.paddle.fastdeploy.app.ui.view.CameraSurfaceView
+            android:id="@+id/sv_preview"
+            android:layout_width="match_parent"
+            android:layout_height="match_parent"
+            android:layout_above="@+id/contral"
+            android:layout_below="@+id/action_bar_main"
+            android:layout_centerInParent="true" />
+
+        <ImageView
+            android:id="@+id/iv_select"
+            android:layout_width="40dp"
+            android:layout_height="40dp"
+            android:layout_alignParentRight="true"
+            android:layout_alignParentBottom="true"
+            android:layout_marginRight="20dp"
+            android:layout_marginBottom="145dp"
+            android:background="@drawable/album_btn"
+            android:scaleType="fitXY" />
+
+        <TextView
+            android:id="@+id/tv_status"
+            android:layout_width="wrap_content"
+            android:layout_height="wrap_content"
+            android:layout_alignParentRight="true"
+            android:layout_marginTop="60dp"
+            android:layout_marginRight="30dp"
+            android:textColor="@color/colorText"
+            android:textSize="@dimen/small_font_size" />
+
+        <RelativeLayout
+            android:layout_width="match_parent"
+            android:layout_height="@dimen/top_bar_height"
+            android:layout_alignParentTop="true"
+            android:background="@color/colorTopBar">
+
+            <ImageButton
+                android:id="@+id/btn_settings"
+                android:layout_width="30dp"
+                android:layout_height="30dp"
+                android:layout_alignParentRight="true"
+                android:layout_centerVertical="true"
+                android:layout_marginRight="10dp"
+                android:background="@null"
+                android:scaleType="fitXY"
+                android:src="@drawable/btn_settings" />
+
+        </RelativeLayout>
+
+        <LinearLayout
+            android:id="@+id/contral"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:layout_alignParentBottom="true"
+            android:background="@color/colorBottomBar"
+            android:orientation="vertical">
+
+            <LinearLayout
+                android:layout_width="match_parent"
+                android:layout_height="@dimen/bottom_bar_top_margin"
+                android:orientation="vertical"></LinearLayout>
+
+            <RelativeLayout
+                android:layout_width="match_parent"
+                android:layout_height="@dimen/large_button_height">
+
+                <ImageButton
+                    android:id="@+id/btn_switch"
+                    android:layout_width="60dp"
+                    android:layout_height="60dp"
+                    android:layout_alignParentLeft="true"
+                    android:layout_centerVertical="true"
+                    android:layout_marginLeft="60dp"
+                    android:background="#00000000"
+                    android:scaleType="fitXY"
+                    android:src="@drawable/switch_side_btn" />
+
+                <ImageButton
+                    android:id="@+id/btn_shutter"
+                    android:layout_width="@dimen/large_button_width"
+                    android:layout_height="@dimen/large_button_height"
+                    android:layout_centerInParent="true"
+                    android:background="@null"
+                    android:scaleType="fitXY"
+                    android:src="@drawable/take_picture_btn" />
+
+                <ImageView
+                    android:id="@+id/realtime_toggle_btn"
+                    android:layout_width="60dp"
+                    android:layout_height="60dp"
+                    android:layout_alignParentRight="true"
+                    android:layout_centerVertical="true"
+                    android:layout_marginRight="60dp"
+                    android:scaleType="fitXY"
+                    android:src="@drawable/realtime_stop_btn" />
+
+            </RelativeLayout>
+
+            <LinearLayout
+                android:layout_width="match_parent"
+                android:layout_height="@dimen/bottom_bar_bottom_margin"
+                android:orientation="vertical"></LinearLayout>
+        </LinearLayout>
+    </RelativeLayout>
+</android.support.constraint.ConstraintLayout>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/layout/classification_result_page.xml

+<?xml version="1.0" encoding="utf-8"?>
+<FrameLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent">
+
+    <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:background="#FFFFFF"
+        android:orientation="vertical">
+
+        <com.baidu.paddle.fastdeploy.app.ui.layout.ActionBarLayout
+            android:id="@+id/action_bar_result"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:orientation="horizontal">
+
+            <ImageView
+                android:id="@+id/back_in_result"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:cropToPadding="true"
+                android:paddingLeft="40px"
+                android:paddingTop="60px"
+                android:paddingRight="60px"
+                android:paddingBottom="40px"
+                android:src="@drawable/back_btn" />
+
+            <TextView
+                android:id="@+id/model_name"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_centerHorizontal="true"
+                android:layout_marginTop="50px"
+                android:textColor="@color/textColor"
+                android:textSize="@dimen/action_btn_text_size" />
+        </com.baidu.paddle.fastdeploy.app.ui.layout.ActionBarLayout>
+
+        <FrameLayout
+            android:layout_width="match_parent"
+            android:layout_height="700px">
+
+            <ImageView
+                android:id="@+id/result_image"
+                android:layout_width="match_parent"
+                android:layout_height="match_parent"
+                android:background="@color/bk_result_image_padding" />
+
+        </FrameLayout>
+
+        <TextView
+            android:layout_width="wrap_content"
+            android:layout_height="wrap_content"
+            android:layout_marginLeft="40px"
+            android:layout_marginTop="26px"
+            android:layout_marginBottom="20px"
+            android:text="@string/result_label"
+            android:textColor="@color/bk_black"
+            android:textSize="56px"
+            android:visibility="visible" />
+
+        <LinearLayout
+            android:id="@+id/result_seekbar_section"
+            android:layout_width="match_parent"
+            android:layout_height="130px"
+            android:layout_marginLeft="@dimen/result_list_padding_lr"
+            android:layout_marginRight="@dimen/result_list_padding_lr"
+            android:layout_marginBottom="@dimen/result_list_gap_width"
+            android:background="@drawable/result_page_border_section_bk"
+            android:visibility="visible">
+
+            <TextView
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_gravity="center_vertical"
+                android:layout_weight="2"
+                android:paddingLeft="30px"
+                android:text="@string/result_table_header_confidence"
+                android:textColor="@color/table_result_tableheader_text_color"
+                android:textSize="@dimen/result_list_view_text_size" />
+
+            <SeekBar
+                android:id="@+id/confidence_seekbar"
+                android:layout_width="220dp"
+                android:layout_height="wrap_content"
+                android:layout_gravity="center_vertical"
+                android:layout_weight="6"
+                android:focusable="false"
+                android:maxHeight="8px"
+                android:progressDrawable="@drawable/seekbar_progress_result"
+                android:splitTrack="false"
+                android:thumb="@drawable/seekbar_handle" />
+
+            <TextView
+                android:id="@+id/seekbar_text"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_gravity="center_vertical"
+                android:layout_weight="1"
+                android:paddingRight="30px"
+                android:textSize="@dimen/result_list_view_text_size"
+
+                />
+        </LinearLayout>
+
+        <LinearLayout
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:layout_marginLeft="@dimen/result_list_padding_lr"
+            android:layout_marginRight="@dimen/result_list_padding_lr"
+            android:layout_marginBottom="@dimen/result_list_gap_width"
+            android:background="@drawable/result_page_border_section_bk"
+            android:visibility="visible">
+
+            <TextView
+                style="@style/list_result_view_tablehead_style"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:text="@string/result_table_header_index"
+                android:textColor="@color/table_result_tableheader_text_color" />
+
+            <TextView
+                style="@style/list_result_view_tablehead_style"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:text="@string/result_table_header_name"
+                android:textColor="@color/table_result_tableheader_text_color" />
+
+            <TextView
+                style="@style/list_result_view_tablehead_style"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_weight="0.4"
+                android:gravity="right"
+                android:text="@string/result_table_header_confidence"
+                android:textColor="@color/table_result_tableheader_text_color" />
+        </LinearLayout>
+
+        <FrameLayout
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content">
+
+            <ScrollView
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:layout_marginBottom="15px"
+                android:paddingLeft="@dimen/result_list_padding_lr"
+                android:paddingRight="@dimen/result_list_padding_lr">
+
+                <com.baidu.paddle.fastdeploy.app.ui.view.ResultListView
+                    android:id="@+id/result_list_view"
+                    android:layout_width="match_parent"
+                    android:layout_height="700px"
+                    android:divider="#FFFFFF"
+                    android:dividerHeight="@dimen/result_list_gap_width"></com.baidu.paddle.fastdeploy.app.ui.view.ResultListView>
+            </ScrollView>
+
+        </FrameLayout>
+    </LinearLayout>
+</FrameLayout>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/layout/classification_result_page_item.xml

+<?xml version="1.0" encoding="utf-8"?>
+<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:orientation="horizontal"
+    android:layout_width="match_parent"
+    android:layout_height="wrap_content"
+    android:background="@drawable/result_page_border_section_bk">
+
+    <TextView
+        android:id="@+id/index"
+        style="@style/list_result_view_item_style"
+        android:layout_width="wrap_content"
+        android:layout_weight="0.2" />
+
+    <TextView
+        android:id="@+id/name"
+        style="@style/list_result_view_item_style"
+        android:layout_width="wrap_content"
+        android:layout_weight="0.6"
+        android:maxWidth="300px" />
+
+    <TextView
+        android:id="@+id/confidence"
+        style="@style/list_result_view_item_style"
+        android:layout_weight="0.2"
+        android:layout_width="wrap_content" />
+</LinearLayout>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/values/arrays.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <string-array name="cpu_thread_num_entries">
+        <item>1 threads</item>
+        <item>2 threads</item>
+        <item>4 threads</item>
+        <item>8 threads</item>
+    </string-array>
+    <string-array name="cpu_thread_num_values">
+        <item>1</item>
+        <item>2</item>
+        <item>4</item>
+        <item>8</item>
+    </string-array>
+    <string-array name="cpu_power_mode_entries">
+        <item>HIGH(only big cores)</item>
+        <item>LOW(only LITTLE cores)</item>
+        <item>FULL(all cores)</item>
+        <item>NO_BIND(depends on system)</item>
+        <item>RAND_HIGH</item>
+        <item>RAND_LOW</item>
+    </string-array>
+    <string-array name="cpu_power_mode_values">
+        <item>LITE_POWER_HIGH</item>
+        <item>LITE_POWER_LOW</item>
+        <item>LITE_POWER_FULL</item>
+        <item>LITE_POWER_NO_BIND</item>
+        <item>LITE_POWER_RAND_HIGH</item>
+        <item>LITE_POWER_RAND_LOW</item>
+    </string-array>
+    <string-array name="enable_lite_fp16_mode_entries">
+        <item>true</item>
+        <item>false</item>
+    </string-array>
+    <string-array name="enable_lite_fp16_mode_values">
+        <item>true</item>
+        <item>false</item>
+    </string-array>
+</resources>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/values/colors.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="colorPrimary">#008577</color>
+    <color name="colorPrimaryDark">#00574B</color>
+    <color name="colorAccent">#D81B60</color>
+    <color name="colorWindow">#FF000000</color>
+    <color name="colorTopBar">#00000000</color>
+    <color name="colorBottomBar">#00000000</color>
+    <color name="colorText">#FFFFFFFF</color>
+
+    <color name="bk_black">#000000</color>
+    <color name="bk_blue">#3B85F5</color>
+    <color name="textColorHighlight">#F5A623</color>
+    <color name="textColor">#FFFFFF</color>
+
+    <color name="bk_result_image_padding">#EEEEEE</color>
+
+    <color name="table_result_item_text_color">#3B85F5</color>
+    <color name="table_result_tableheader_text_color">#333333</color>
+    <color name="result_section_border_color">#E5E5E5</color>
+    <color name="result_popview_tablebody_bk">#3b85f5</color>
+</resources>

deploy/fastdeploy/android/app/src/main/res/values/dimens.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <dimen name="bottom_bar_top_margin">26dp</dimen>
+    <dimen name="bottom_bar_bottom_margin">36dp</dimen>
+    <dimen name="bottom_bar_left_right_margin">34dp</dimen>
+    <dimen name="top_bar_height">60dp</dimen>
+    <dimen name="top_bar_left_right_margin">16dp</dimen>
+    <dimen name="large_button_width">67dp</dimen>
+    <dimen name="large_button_height">67dp</dimen>
+    <dimen name="medium_button_width">56dp</dimen>
+    <dimen name="medium_button_height">56dp</dimen>
+    <dimen name="small_button_width">46dp</dimen>
+    <dimen name="small_button_height">46dp</dimen>
+    <dimen name="large_font_size">32dp</dimen>
+    <dimen name="medium_font_size">24dp</dimen>
+    <dimen name="small_font_size">16dp</dimen>
+</resources>

deploy/fastdeploy/android/app/src/main/res/values/strings.xml

+<resources>
+    <!-- Default App name -->
+    <string name="app_name">EasyEdge</string>
+    <!-- Other App name -->
+    <string name="detection_app_name">EasyEdge</string>
+    <string name="ocr_app_name">EasyEdge</string>
+    <string name="classification_app_name">EasyEdge</string>
+    <string name="facedet_app_name">EasyEdge</string>
+    <string name="segmentation_app_name">EasyEdge</string>
+    <!-- Keys for PreferenceScreen -->
+    <string name="CHOOSE_PRE_INSTALLED_MODEL_KEY">CHOOSE_INSTALLED_MODEL_KEY</string>
+    <string name="MODEL_DIR_KEY">MODEL_DIR_KEY</string>
+    <string name="LABEL_PATH_KEY">LABEL_PATH_KEY</string>
+    <string name="CPU_THREAD_NUM_KEY">CPU_THREAD_NUM_KEY</string>
+    <string name="CPU_POWER_MODE_KEY">CPU_POWER_MODE_KEY</string>
+    <string name="SCORE_THRESHOLD_KEY">SCORE_THRESHOLD_KEY</string>
+    <string name="ENABLE_LITE_FP16_MODE_KEY">ENABLE_LITE_FP16_MODE_KEY</string>
+    <!-- Common default values ... -->
+    <string name="CPU_THREAD_NUM_DEFAULT">2</string>
+    <string name="CPU_POWER_MODE_DEFAULT">LITE_POWER_HIGH</string>
+    <string name="SCORE_THRESHOLD_DEFAULT">0.4</string>
+    <string name="SCORE_THRESHOLD_CLASSIFICATION">0.1</string>
+    <string name="SCORE_THRESHOLD_FACEDET">0.25</string>
+    <string name="ENABLE_LITE_FP16_MODE_DEFAULT">true</string>
+    <!--Other values-->
+    <!-- Detection model & Label paths & other values ... -->
+    <string name="DETECTION_MODEL_DIR_DEFAULT">models/picodet_s_320_coco_lcnet</string>
+    <string name="DETECTION_LABEL_PATH_DEFAULT">labels/coco_label_list.txt</string>
+    <!-- PP-OCRv2 & PP-OCRv3 values ... -->
+    <string name="OCR_MODEL_DIR_DEFAULT">models</string>
+    <string name="OCR_REC_LABEL_DEFAULT">labels/ppocr_keys_v1.txt</string>
+    <!-- classification values ... -->
+    <string name="CLASSIFICATION_MODEL_DIR_DEFAULT">models/MobileNetV1_x0_25_infer</string>
+    <string name="CLASSIFICATION_LABEL_PATH_DEFAULT">labels/imagenet1k_label_list.txt</string>
+    <!-- facedet values ... -->
+    <string name="FACEDET_MODEL_DIR_DEFAULT">models/scrfd_500m_bnkps_shape320x320_pd</string>
+    <!-- segmentation values ... -->
+    <string name="SEGMENTATION_MODEL_DIR_DEFAULT">models/portrait_pp_humansegv2_lite_256x144_inference_model</string>
+    <!-- Other resources values-->
+    <string name="action_bar_take_photo">拍照识别</string>
+    <string name="action_bar_realtime">实时识别</string>
+    <string name="action_bar_back">&lt;</string>
+    <string name="action_bar_model_name">模型名称</string>
+    <string name="result_label">识别结果</string>
+    <string name="result_table_header_index">序号</string>
+    <string name="result_table_header_name">名称</string>
+    <string name="result_table_header_confidence">置信度</string>
+    <string name="operation_confidence_control">阈值控制</string>
+    <string name="operation_retry">重新识别</string>
+    <string name="operation_save">保存结果</string>
+</resources>

deploy/fastdeploy/android/app/src/main/res/values/styles.xml

+<resources>
+
+    <!-- Base application theme. -->
+    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
+        <!-- Customize your theme here. -->
+        <item name="colorPrimary">@color/colorPrimary</item>
+        <item name="colorPrimaryDark">@color/colorPrimaryDark</item>
+        <item name="colorAccent">@color/colorAccent</item>
+        <item name="actionOverflowMenuStyle">@style/OverflowMenuStyle</item>
+    </style>
+
+    <style name="OverflowMenuStyle" parent="Widget.AppCompat.Light.PopupMenu.Overflow">
+        <item name="overlapAnchor">false</item>
+    </style>
+
+    <style name="AppTheme.NoActionBar">
+        <item name="windowActionBar">false</item>
+        <item name="windowNoTitle">true</item>
+    </style>
+
+    <style name="AppTheme.AppBarOverlay" parent="ThemeOverlay.AppCompat.Dark.ActionBar"/>
+
+    <style name="AppTheme.PopupOverlay" parent="ThemeOverlay.AppCompat.Light"/>
+
+    <style name="list_result_view_item_style">
+        <item name="android:textColor">@color/table_result_item_text_color</item>
+        <item name="android:layout_height">wrap_content</item>
+        <item name="android:layout_weight">1</item>
+        <item name="android:gravity">left</item>
+        <item name="android:padding">30px</item>
+    </style>
+
+    <style name="list_result_popview_item_style">
+        <item name="android:textColor">@color/textColor</item>
+        <item name="android:layout_height">wrap_content</item>
+        <item name="android:gravity">left</item>
+        <item name="android:padding">15px</item>
+        <item name="android:background">@color/result_popview_tablebody_bk</item>
+        <item name="android:layout_width">wrap_content</item>
+        <item name="android:alpha">0.5</item>
+    </style>
+
+    <style name="list_result_view_tablehead_style">
+        <item name="android:textColor">@color/table_result_item_text_color</item>
+        <item name="android:layout_height">wrap_content</item>
+        <item name="android:layout_weight">1</item>
+        <item name="android:gravity">left</item>
+        <item name="android:padding">15px</item>
+    </style>
+
+    <style name="list_result_popview_tablehead_style">
+        <item name="android:textColor">@color/textColor</item>
+        <item name="android:layout_height">wrap_content</item>
+        <item name="android:layout_weight">1</item>
+        <item name="android:gravity">left</item>
+        <item name="android:padding">20px</item>
+    </style>
+
+    <style name="action_btn">
+        <item name="android:textColor">@color/textColor</item>
+        <item name="android:background">@color/bk_black</item>
+    </style>
+
+    <style name="action_btn_selected">
+        <item name="android:textColor">@color/textColorHighlight</item>
+        <item name="android:background">@color/bk_black</item>
+    </style>
+
+
+</resources>

deploy/fastdeploy/android/app/src/main/res/values/values.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <dimen name="action_btn_size">120dp</dimen>
+    <dimen name="action_btn_text_size">46px</dimen>
+
+    <dimen name="operation_btn_margin_top_take_picture">126px</dimen>
+    <dimen name="operation_btn_margin_top">136px</dimen>
+
+    <dimen name="result_list_view_text_size">46px</dimen>
+
+    <dimen name="result_list_popview_text_size">36px</dimen>
+
+    <dimen name="result_list_padding_lr">15dp</dimen>
+
+    <dimen name="result_list_gap_width">15dp</dimen>
+
+</resources>
\ No newline at end of file

deploy/fastdeploy/android/app/src/main/res/xml/classification_settings.xml

+<?xml version="1.0" encoding="utf-8"?>
+<PreferenceScreen xmlns:android="http://schemas.android.com/apk/res/android">
+    <ListPreference
+        android:defaultValue="@string/CLASSIFICATION_MODEL_DIR_DEFAULT"
+        android:key="@string/CHOOSE_PRE_INSTALLED_MODEL_KEY"
+        android:negativeButtonText="@null"
+        android:positiveButtonText="@null"
+        android:title="Choose Pre-Installed Models" />
+    <EditTextPreference
+        android:defaultValue="@string/CLASSIFICATION_MODEL_DIR_DEFAULT"
+        android:key="@string/MODEL_DIR_KEY"
+        android:title="Model Dir" />
+    <EditTextPreference
+        android:defaultValue="@string/CLASSIFICATION_LABEL_PATH_DEFAULT"
+        android:key="@string/LABEL_PATH_KEY"
+        android:title="Label Path" />
+    <ListPreference
+        android:defaultValue="@string/CPU_THREAD_NUM_DEFAULT"
+        android:entries="@array/cpu_thread_num_entries"
+        android:entryValues="@array/cpu_thread_num_values"
+        android:key="@string/CPU_THREAD_NUM_KEY"
+        android:negativeButtonText="@null"
+        android:positiveButtonText="@null"
+        android:title="CPU Thread Num" />
+    <ListPreference
+        android:defaultValue="@string/CPU_POWER_MODE_DEFAULT"
+        android:entries="@array/cpu_power_mode_entries"
+        android:entryValues="@array/cpu_power_mode_values"
+        android:key="@string/CPU_POWER_MODE_KEY"
+        android:negativeButtonText="@null"
+        android:positiveButtonText="@null"
+        android:title="CPU Power Mode" />
+    <EditTextPreference
+        android:key="@string/SCORE_THRESHOLD_KEY"
+        android:defaultValue="@string/SCORE_THRESHOLD_CLASSIFICATION"
+        android:title="Score Threshold: (0.0, 1.0)" />
+    <ListPreference
+        android:defaultValue="@string/ENABLE_LITE_FP16_MODE_DEFAULT"
+        android:entries="@array/enable_lite_fp16_mode_entries"
+        android:entryValues="@array/enable_lite_fp16_mode_values"
+        android:key="@string/ENABLE_LITE_FP16_MODE_KEY"
+        android:negativeButtonText="@null"
+        android:positiveButtonText="@null"
+        android:title="Enable Lite FP16" />
+</PreferenceScreen>
\ No newline at end of file

deploy/fastdeploy/android/build.gradle

+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+//plugins {
+//    id 'com.android.application' version '7.2.2' apply false
+//    id 'com.android.library' version '7.2.2' apply false
+//}
+//
+//task clean(type: Delete) {
+//    delete rootProject.buildDir
+//}
+
+buildscript {
+    repositories {
+        google()
+        jcenter()
+        // mavenCentral()
+
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:7.2.2'
+
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        jcenter()
+        // mavenCentral()
+
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

deploy/fastdeploy/android/gradle.properties

+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx3096m
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true

deploy/fastdeploy/android/gradle/wrapper/gradle-wrapper.properties

+#Sat Oct 08 17:24:34 CST 2022
+distributionBase=GRADLE_USER_HOME
+distributionUrl=https\://services.gradle.org/distributions/gradle-7.3.3-bin.zip
+distributionPath=wrapper/dists
+zipStorePath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME

deploy/fastdeploy/android/gradlew

+#!/usr/bin/env sh
+
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=`expr $i + 1`
+    done
+    case $i in
+        0) set -- ;;
+        1) set -- "$args0" ;;
+        2) set -- "$args0" "$args1" ;;
+        3) set -- "$args0" "$args1" "$args2" ;;
+        4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=`save "$@"`
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+exec "$JAVACMD" "$@"

deploy/fastdeploy/android/gradlew.bat

+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

deploy/fastdeploy/android/local.properties

+## This file must *NOT* be checked into Version Control Systems,
+# as it contains information specific to your local configuration.
+#
+# Location of the SDK. This is only used by Gradle.
+# For customization when using a Version Control System, please read the
+# header note.
+#Wed Nov 23 11:20:41 CST 2022
+sdk.dir=D\:\\androidsdk

deploy/fastdeploy/android/settings.gradle

+include ':app'

deploy/fastdeploy/ascend/README.md

+../../../docs/zh_CN/fastdeploy/ascend/README.md
\ No newline at end of file

deploy/fastdeploy/ascend/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/ascend/cpp/README.md

+../../../../docs/zh_CN/fastdeploy/ascend/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/ascend/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void AscendInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseAscend();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image " << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  AscendInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/ascend/python/README.md

+../../../../docs/zh_CN/fastdeploy/ascend/python/README.md
\ No newline at end of file

deploy/fastdeploy/ascend/python/infer.py

+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+    option.use_ascend()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)

deploy/fastdeploy/cpu-gpu/README.md

+../../../docs/zh_CN/fastdeploy/cpu-gpu/README.md
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/c/CMakeLists.txt

+PROJECT(infer_demo C)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.c)
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/cpu-gpu/c/README.md

+../../../../docs/zh_CN/fastdeploy/cpu-gpu/c/README.md
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/c/infer.c

+// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "fastdeploy_capi/vision.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void CpuInfer(const char *model_dir, const char *image_file) {
+  char model_file[100];
+  char params_file[100];
+  char config_file[100];
+  int max_size = 99;
+  snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "inference.pdmodel");
+  snprintf(params_file, max_size, "%s%c%s", model_dir, sep,
+           "inference.pdiparams");
+  snprintf(config_file, max_size, "%s%c%s", model_dir, sep,
+           "inference_cls.yaml");
+
+  FD_C_RuntimeOptionWrapper *option = FD_C_CreateRuntimeOptionWrapper();
+  FD_C_RuntimeOptionWrapperUseCpu(option);
+
+  FD_C_PaddleClasModelWrapper *model = FD_C_CreatePaddleClasModelWrapper(
+      model_file, params_file, config_file, option, FD_C_ModelFormat_PADDLE);
+
+  if (!FD_C_PaddleClasModelWrapperInitialized(model)) {
+    printf("Failed to initialize.\n");
+    FD_C_DestroyRuntimeOptionWrapper(option);
+    FD_C_DestroyPaddleClasModelWrapper(model);
+    return;
+  }
+
+  FD_C_Mat im = FD_C_Imread(image_file);
+
+  FD_C_ClassifyResult *result =
+      (FD_C_ClassifyResult *)malloc(sizeof(FD_C_ClassifyResult));
+
+  if (!FD_C_PaddleClasModelWrapperPredict(model, im, result)) {
+    printf("Failed to predict.\n");
+    FD_C_DestroyRuntimeOptionWrapper(option);
+    FD_C_DestroyPaddleClasModelWrapper(model);
+    FD_C_DestroyMat(im);
+    free(result);
+    return;
+  }
+
+  // print res
+  char res[2000];
+  FD_C_ClassifyResultStr(result, res);
+  printf("%s", res);
+  FD_C_DestroyRuntimeOptionWrapper(option);
+  FD_C_DestroyPaddleClasModelWrapper(model);
+  FD_C_DestroyClassifyResult(result);
+  FD_C_DestroyMat(im);
+}
+
+void GpuInfer(const char *model_dir, const char *image_file) {
+  char model_file[100];
+  char params_file[100];
+  char config_file[100];
+  int max_size = 99;
+  snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "inference.pdmodel");
+  snprintf(params_file, max_size, "%s%c%s", model_dir, sep,
+           "inference.pdiparams");
+  snprintf(config_file, max_size, "%s%c%s", model_dir, sep,
+           "inference_cls.yaml");
+
+  FD_C_RuntimeOptionWrapper *option = FD_C_CreateRuntimeOptionWrapper();
+  FD_C_RuntimeOptionWrapperUseGpu(option, 0);
+
+  FD_C_PaddleClasModelWrapper *model = FD_C_CreatePaddleClasModelWrapper(
+      model_file, params_file, config_file, option, FD_C_ModelFormat_PADDLE);
+
+  if (!FD_C_PaddleClasModelWrapperInitialized(model)) {
+    printf("Failed to initialize.\n");
+    FD_C_DestroyRuntimeOptionWrapper(option);
+    FD_C_DestroyPaddleClasModelWrapper(model);
+    return;
+  }
+
+  FD_C_Mat im = FD_C_Imread(image_file);
+
+  FD_C_ClassifyResult *result =
+      (FD_C_ClassifyResult *)malloc(sizeof(FD_C_ClassifyResult));
+
+  if (!FD_C_PaddleClasModelWrapperPredict(model, im, result)) {
+    printf("Failed to predict.\n");
+    FD_C_DestroyRuntimeOptionWrapper(option);
+    FD_C_DestroyPaddleClasModelWrapper(model);
+    FD_C_DestroyMat(im);
+    free(result);
+    return;
+  }
+
+  // print res
+  char res[2000];
+  FD_C_ClassifyResultStr(result, res);
+  printf("%s", res);
+  FD_C_DestroyRuntimeOptionWrapper(option);
+  FD_C_DestroyPaddleClasModelWrapper(model);
+  FD_C_DestroyClassifyResult(result);
+  FD_C_DestroyMat(im);
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 4) {
+    printf("Usage: infer_demo path/to/model_dir path/to/image run_option, "
+           "e.g ./infer_model ./ppyoloe_model_dir ./test.jpeg 0"
+           "\n");
+    printf(
+        "The data type of run_option is int, 0: run with cpu; 1: run with gpu"
+        "\n");
+    return -1;
+  }
+
+  if (atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  }
+  return 0;
+}

deploy/fastdeploy/cpu-gpu/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/cpu-gpu/cpp/README.md

+../../../../docs/zh_CN/fastdeploy/cpu-gpu/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &model_dir, const std::string &image_file,
+                  const fastdeploy::RuntimeOption &option) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  // print res
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 4) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
+                 "e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
+              << std::endl;
+    return -1;
+  }
+
+  fastdeploy::RuntimeOption option;
+  int flag = std::atoi(argv[3]);
+
+  if (flag == 0) {
+    option.UseCpu();
+    option.UsePaddleBackend(); // Paddle Inference
+  } else if (flag == 1) {
+    option.UseCpu();
+    option.UseOpenVINOBackend(); // OpenVINO
+  } else if (flag == 2) {
+    option.UseCpu();
+    option.UseOrtBackend(); // ONNX Runtime
+  } else if (flag == 3) {
+    option.UseCpu();
+    option.UseLiteBackend(); // Paddle Lite
+  } else if (flag == 4) {
+    option.UseGpu();
+    option.UsePaddleBackend(); // Paddle Inference
+  } else if (flag == 5) {
+    option.UseGpu();
+    option.UsePaddleInferBackend();
+    option.paddle_infer_option.enable_trt = true;
+  } else if (flag == 6) {
+    option.UseGpu();
+    option.UseOrtBackend(); // ONNX Runtime
+  } else if (flag == 7) {
+    option.UseGpu();
+    option.UseTrtBackend(); // TensorRT
+  }
+
+  std::string model_dir = argv[1];
+  std::string image_dir = argv[2];
+  InitAndInfer(model_dir, image_dir, option);
+}

deploy/fastdeploy/cpu-gpu/cpp/ocr.sh

+rm -rf build
+mkdir build
+
+cd build
+
+#/xieyunyao/project/FastDeploy
+
+cmake .. -DFASTDEPLOY_INSTALL_DIR=/xieyunyao/project/fastdeploy-linux-x64-gpu-0.0.0
+
+make -j

deploy/fastdeploy/cpu-gpu/csharp/CMakeLists.txt

+PROJECT(infer_demo CSharp)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# Set the C# language version (defaults to 3.0 if not set).
+set(CMAKE_CSharp_FLAGS "/langversion:10")
+set(CMAKE_DOTNET_TARGET_FRAMEWORK "net6.0")
+set(CMAKE_DOTNET_SDK "Microsoft.NET.Sdk")
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeployCSharp.cmake)
+
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cs)
+
+set_property(TARGET infer_demo PROPERTY VS_DOTNET_REFERENCES
+    ${FASTDEPLOY_DOTNET_REFERENCES}
+)
+
+set_property(TARGET infer_demo
+    PROPERTY VS_PACKAGE_REFERENCES  ${FASTDEPLOY_PACKAGE_REFERENCES})

deploy/fastdeploy/cpu-gpu/csharp/README.md

+../../../../docs/zh_CN/fastdeploy/cpu-gpu/csharp/README.md
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/csharp/infer.cs

+// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+using System;
+using System.IO;
+using System.Runtime.InteropServices;
+using OpenCvSharp;
+using fastdeploy;
+
+namespace Test
+{
+    public class TestPaddleClas
+    {
+        public static void Main(string[] args)
+        {
+            if (args.Length < 3) {
+                Console.WriteLine(
+                    "Usage: infer_demo path/to/model_dir path/to/image run_option, " +
+                    "e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
+                );
+                Console.WriteLine( "The data type of run_option is int, 0: run with cpu; 1: run with gpu");
+                return;
+            }
+            string model_dir = args[0];
+            string image_path = args[1];
+            string model_file = model_dir + "\\" + "inference.pdmodel";
+            string params_file = model_dir + "\\" + "inference.pdiparams";
+            string config_file = model_dir + "\\" + "inference_cls.yaml";
+            RuntimeOption runtimeoption = new RuntimeOption();
+            int device_option = Int32.Parse(args[2]);
+            if(device_option==0){
+                runtimeoption.UseCpu();
+            }else{
+                runtimeoption.UseGpu();
+            }
+            fastdeploy.vision.classification.PaddleClasModel model = new fastdeploy.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE);
+            if(!model.Initialized()){
+                Console.WriteLine("Failed to initialize.\n");
+            }
+            Mat image = Cv2.ImRead(image_path);
+            fastdeploy.vision.ClassifyResult res = model.Predict(image);
+            Console.WriteLine(res.ToString());
+
+        }
+
+    }
+}
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/python/README.md

+../../../../docs/zh_CN/fastdeploy/cpu-gpu/python/README.md
\ No newline at end of file

deploy/fastdeploy/cpu-gpu/python/infer.py

+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
+    )
+    parser.add_argument(
+        "--device_id",
+        type=int,
+        default=0,
+        help="Define which GPU card used to run model.")
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="default",
+        help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
+    )
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu(args.device_id)
+
+    if args.backend.lower() == "trt":
+        assert args.device.lower(
+        ) == "gpu", "TensorRT backend require inference on device GPU."
+        option.use_trt_backend()
+
+    elif args.backend.lower() == "pptrt":
+        assert args.device.lower(
+        ) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
+        option.use_paddle_infer_backend()
+        option.paddle_infer_option.enable_trt = True
+
+    elif args.backend.lower() == "ort":
+        option.use_ort_backend()
+
+    elif args.backend.lower() == "paddle":
+        option.use_paddle_infer_backend()
+
+    elif args.backend.lower() == "openvino":
+        assert args.device.lower(
+        ) == "cpu", "OpenVINO backend require inference on device CPU."
+        option.use_openvino_backend()
+
+    elif args.backend.lower() == "pplite":
+        assert args.device.lower(
+        ) == "cpu", "Paddle Lite backend require inference on device CPU."
+        option.use_lite_backend()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)

deploy/fastdeploy/graphcore/README.md

+../../../docs/zh_CN/fastdeploy/graphcore/README.md
\ No newline at end of file

deploy/fastdeploy/graphcore/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/graphcore/cpp/README.md

+../../../../docs/zh_CN/fastdeploy/graphcore/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/graphcore/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void IPUInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseIPU();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image " << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  IPUInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/graphcore/python/README.md

+../../../../docs/zh_CN/fastdeploy/graphcore/python/README.md
\ No newline at end of file

deploy/fastdeploy/graphcore/python/infer.py

+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+    option.use_ipu()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)

deploy/fastdeploy/kunlun/README.md

+../../../docs/zh_CN/fastdeploy/kunlun/README.md
\ No newline at end of file

deploy/fastdeploy/kunlun/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/kunlun/cpp/README.md

+../../../../docs/zh_CN/fastdeploy/kunlun/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/kunlun/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void KunlunInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseKunlunXin();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image " << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  KunlunInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/kunlun/python/README.md

+../../../../docs/zh_CN/fastdeploy/kunlun/python/README.md
\ No newline at end of file

deploy/fastdeploy/kunlun/python/infer.py

+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+    option.use_kunlunxin()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)

deploy/fastdeploy/quantize/README.md

+../../../docs/zh_CN/fastdeploy/quantize/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rknpu2/README.md

+../../../../../docs/zh_CN/fastdeploy/rockchip/rknpu2/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rknpu2/cpp/CMakeLists.txt

+CMAKE_MINIMUM_REQUIRED(VERSION 3.10)
+project(rknpu_test)
+
+set(CMAKE_CXX_STANDARD 14)
+
+# 指定下载解压后的fastdeploy库路径
+set(FASTDEPLOY_INSTALL_DIR "thirdpartys/fastdeploy-0.0.3")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeployConfig.cmake)
+include_directories(${FastDeploy_INCLUDE_DIRS})
+add_executable(rknpu_test infer.cc)
+target_link_libraries(rknpu_test
+        ${FastDeploy_LIBS}
+        )
+
+
+set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
+
+install(TARGETS rknpu_test DESTINATION ./)
+
+install(DIRECTORY ppclas_model_dir DESTINATION ./)
+install(DIRECTORY images DESTINATION ./)
+
+file(GLOB FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/*)
+message("${FASTDEPLOY_LIBS}")
+install(PROGRAMS ${FASTDEPLOY_LIBS} DESTINATION lib)
+
+file(GLOB ONNXRUNTIME_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/onnxruntime/lib/*)
+install(PROGRAMS ${ONNXRUNTIME_LIBS} DESTINATION lib)
+
+install(DIRECTORY ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/opencv/lib DESTINATION ./)
+
+file(GLOB PADDLETOONNX_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/paddle2onnx/lib/*)
+install(PROGRAMS ${PADDLETOONNX_LIBS} DESTINATION lib)
+
+file(GLOB RKNPU2_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/rknpu2_runtime/RK3588/lib/*)
+install(PROGRAMS ${RKNPU2_LIBS} DESTINATION lib)
\ No newline at end of file

deploy/fastdeploy/rockchip/rknpu2/cpp/README.md

+../../../../../docs/zh_CN/fastdeploy/rockchip/rknpu2/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rknpu2/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+
+void RKNPU2Infer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + "/ResNet50_vd_infer_rk3588.rknn";
+  auto params_file = "";
+  auto config_file = model_dir + "/inference_cls.yaml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseRKNPU2();
+
+  auto format = fastdeploy::ModelFormat::RKNN;
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option, format);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+  model.GetPreprocessor().DisablePermute();
+  fastdeploy::TimeCounter tc;
+  tc.Start();
+  auto im = cv::imread(image_file);
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  // print res
+  std::cout << res.Str() << std::endl;
+  tc.End();
+  tc.PrintInfo("PPClas in RKNPU2");
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout
+        << "Usage: rknpu_test path/to/model_dir path/to/image run_option, "
+           "e.g ./rknpu_test ./ppclas_model_dir "
+           "./images/ILSVRC2012_val_00000010.jpeg"
+        << std::endl;
+    return -1;
+  }
+  RKNPU2Infer(argv[1], argv[2]);
+  return 0;
+}

deploy/fastdeploy/rockchip/rknpu2/python/README.md

+../../../../../docs/zh_CN/fastdeploy/rockchip/rknpu2/python/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rknpu2/python/infer.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_file", required=True, help="Path of rknn model.")
+    parser.add_argument("--config_file", required=True, help="Path of config.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+
+    model_file = args.model_file
+    params_file = ""
+    config_file = args.config_file
+    # 配置runtime，加载模型
+    runtime_option = fd.RuntimeOption()
+    runtime_option.use_rknpu2()
+    model = fd.vision.classification.ResNet50vd(
+        model_file,
+        params_file,
+        config_file,
+        runtime_option=runtime_option,
+        model_format=fd.ModelFormat.RKNN)
+    # 禁用通道转换    
+    model.preprocessor.disable_permute()
+    im = cv2.imread(args.image)
+    result = model.predict(im, topk=1)
+    print(result)

deploy/fastdeploy/rockchip/rknpu2/rknpu2_tools/config/ResNet50_vd_infer_rknn.yaml

+model_path: ./ResNet50_vd_infer/ResNet50_vd_infer.onnx
+output_folder: ./ResNet50_vd_infer
+mean:
+  -
+    - 123.675
+    - 116.28
+    - 103.53
+std:
+  -
+    - 58.395
+    - 57.12
+    - 57.375
+outputs_nodes:
+do_quantization: False
+dataset: "./ResNet50_vd_infer/dataset.txt"

deploy/fastdeploy/rockchip/rknpu2/rknpu2_tools/export.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import yaml
+import argparse
+from rknn.api import RKNN
+
+
+def get_config():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--verbose", default=True, help="rknntoolkit verbose")
+    parser.add_argument("--config_path")
+    parser.add_argument("--target_platform")
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    config = get_config()
+    with open(config.config_path) as file:
+        file_data = file.read()
+        yaml_config = yaml.safe_load(file_data)
+    print(yaml_config)
+    model = RKNN(config.verbose)
+
+    # Config
+    mean_values = yaml_config["mean"]
+    std_values = yaml_config["std"]
+    model.config(
+        mean_values=mean_values,
+        std_values=std_values,
+        target_platform=config.target_platform)
+
+    # Load ONNX model
+    if yaml_config["outputs_nodes"] is None:
+        ret = model.load_onnx(model=yaml_config["model_path"])
+    else:
+        ret = model.load_onnx(
+            model=yaml_config["model_path"],
+            outputs=yaml_config["outputs_nodes"])
+    assert ret == 0, "Load model failed!"
+
+    # Build model
+    ret = model.build(
+        do_quantization=yaml_config["do_quantization"],
+        dataset=yaml_config["dataset"])
+    assert ret == 0, "Build model failed!"
+
+    # Init Runtime
+    ret = model.init_runtime()
+    assert ret == 0, "Init runtime environment failed!"
+
+    # Export
+    if not os.path.exists(yaml_config["output_folder"]):
+        os.mkdir(yaml_config["output_folder"])
+
+    name_list = os.path.basename(yaml_config["model_path"]).split(".")
+    model_base_name = ""
+    for name in name_list[0:-1]:
+        model_base_name += name
+    model_device_name = config.target_platform.lower()
+    if yaml_config["do_quantization"]:
+        model_save_name = model_base_name + "_" + model_device_name + "_quantized" + ".rknn"
+    else:
+        model_save_name = model_base_name + "_" + model_device_name + "_unquantized" + ".rknn"
+    ret = model.export_rknn(
+        os.path.join(yaml_config["output_folder"], model_save_name))
+    assert ret == 0, "Export rknn model failed!"
+    print("Export OK!")

deploy/fastdeploy/rockchip/rv1126/README.md

+../../../../docs/zh_CN/fastdeploy/rockchip/rv1126/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rv1126/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+include_directories(${FastDeploy_INCLUDE_DIRS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
+
+set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/build/install)
+
+install(TARGETS infer_demo DESTINATION ./)
+
+install(DIRECTORY models DESTINATION ./)
+install(DIRECTORY images DESTINATION ./)
+
+file(GLOB_RECURSE FASTDEPLOY_LIBS ${FASTDEPLOY_INSTALL_DIR}/lib/lib*.so*)
+file(GLOB_RECURSE ALL_LIBS ${FASTDEPLOY_INSTALL_DIR}/third_libs/install/lib*.so*)
+list(APPEND ALL_LIBS ${FASTDEPLOY_LIBS})
+install(PROGRAMS ${ALL_LIBS} DESTINATION lib)
+
+file(GLOB ADB_TOOLS run_with_adb.sh)
+install(PROGRAMS ${ADB_TOOLS} DESTINATION ./)

deploy/fastdeploy/rockchip/rv1126/cpp/README.md

+../../../../../docs/zh_CN/fastdeploy/rockchip/rv1126/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/rockchip/rv1126/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "fastdeploy/vision.h"
+#include <string>
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+  fastdeploy::vision::EnableFlyCV();
+  fastdeploy::RuntimeOption option;
+  option.UseTimVX();
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/quant_model "
+                 "path/to/image "
+                 "e.g ./infer_demo ./ResNet50_vd_quant ./test.jpeg"
+              << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  InitAndInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/rockchip/rv1126/cpp/run_with_adb.sh

+#!/bin/bash
+HOST_SPACE=${PWD}
+echo ${HOST_SPACE}
+WORK_SPACE=/data/local/tmp/test
+
+# The first parameter represents the demo name
+DEMO_NAME=image_classification_demo
+if [ -n "$1" ]; then
+  DEMO_NAME=$1
+fi
+
+# The second parameter represents the model name
+MODEL_NAME=mobilenet_v1_fp32_224
+if [ -n "$2" ]; then
+  MODEL_NAME=$2
+fi
+
+# The third parameter indicates the name of the image to be tested
+IMAGE_NAME=0001.jpg
+if [ -n "$3" ]; then
+  IMAGE_NAME=$3
+fi
+
+# The fourth parameter represents the ID of the device
+ADB_DEVICE_NAME=
+if [ -n "$4" ]; then
+  ADB_DEVICE_NAME="-s $4"
+fi
+
+# Set the environment variables required during the running process
+EXPORT_ENVIRONMENT_VARIABLES="export GLOG_v=5; export VIV_VX_ENABLE_GRAPH_TRANSFORM=-pcq:1; export VIV_VX_SET_PER_CHANNEL_ENTROPY=100; export TIMVX_BATCHNORM_FUSION_MAX_ALLOWED_QUANT_SCALE_DEVIATION=300000; export VSI_NN_LOG_LEVEL=5;"
+
+EXPORT_ENVIRONMENT_VARIABLES="${EXPORT_ENVIRONMENT_VARIABLES}export LD_LIBRARY_PATH=${WORK_SPACE}/lib:\$LD_LIBRARY_PATH;"
+
+# Please install adb, and DON'T run this in the docker.
+set -e
+adb $ADB_DEVICE_NAME shell "rm -rf $WORK_SPACE"
+adb $ADB_DEVICE_NAME shell "mkdir -p $WORK_SPACE"
+
+# Upload the demo, librarys, model and test images to the device
+adb $ADB_DEVICE_NAME push ${HOST_SPACE}/lib $WORK_SPACE
+adb $ADB_DEVICE_NAME push ${HOST_SPACE}/${DEMO_NAME} $WORK_SPACE
+adb $ADB_DEVICE_NAME push models $WORK_SPACE
+adb $ADB_DEVICE_NAME push images $WORK_SPACE
+
+# Execute the deployment demo
+adb $ADB_DEVICE_NAME shell "cd $WORK_SPACE; ${EXPORT_ENVIRONMENT_VARIABLES} chmod +x ./${DEMO_NAME}; ./${DEMO_NAME} ./models/${MODEL_NAME} ./images/$IMAGE_NAME"

deploy/fastdeploy/serving/README.md

+../../../docs/zh_CN/fastdeploy/serving/README.md
\ No newline at end of file

deploy/fastdeploy/serving/models/paddlecls/1/README.md

+# PaddleCls Pipeline
+
+The pipeline directory does not have model files, but a version number directory needs to be maintained.

deploy/fastdeploy/serving/models/paddlecls/config.pbtxt

+name: "paddlecls"
+platform: "ensemble"
+max_batch_size: 16
+input [
+  {
+    name: "INPUT"
+    data_type: TYPE_UINT8
+    dims: [ -1, -1, 3 ]
+  }
+]
+output [
+  {
+    name: "CLAS_RESULT"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+ensemble_scheduling {
+  step [
+    {
+      model_name: "preprocess"
+      model_version: 1
+      input_map {
+        key: "preprocess_input"
+        value: "INPUT"
+      }
+      output_map {
+        key: "preprocess_output"
+        value: "RUNTIME_INPUT"
+      }
+    },
+    {
+      model_name: "runtime"
+      model_version: 1
+      input_map {
+        key: "inputs"
+        value: "RUNTIME_INPUT"
+      }
+      output_map {
+        key: "save_infer_model/scale_0.tmp_1"
+        value: "RUNTIME_OUTPUT"
+      }
+    },
+    {
+      model_name: "postprocess"
+      model_version: 1
+      input_map {
+        key: "post_input"
+        value: "RUNTIME_OUTPUT"
+      }
+      output_map {
+        key: "post_output"
+        value: "CLAS_RESULT"
+      }
+    }
+  ]
+}
\ No newline at end of file

deploy/fastdeploy/serving/models/postprocess/1/model.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import numpy as np
+import time
+
+import fastdeploy as fd
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to intialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        # You must parse model_config. JSON string is not parsed here
+        self.model_config = json.loads(args['model_config'])
+        print("model_config:", self.model_config)
+
+        self.input_names = []
+        for input_config in self.model_config["input"]:
+            self.input_names.append(input_config["name"])
+        print("postprocess input names:", self.input_names)
+
+        self.output_names = []
+        self.output_dtype = []
+        for output_config in self.model_config["output"]:
+            self.output_names.append(output_config["name"])
+            dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
+            self.output_dtype.append(dtype)
+        print("postprocess output names:", self.output_names)
+
+        self.postprocess_ = fd.vision.classification.PaddleClasPostprocessor()
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse.
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        responses = []
+        for request in requests:
+            infer_outputs = pb_utils.get_input_tensor_by_name(
+                request, self.input_names[0])
+            infer_outputs = infer_outputs.as_numpy()
+
+            results = self.postprocess_.run([infer_outputs, ])
+            r_str = fd.vision.utils.fd_result_to_json(results)
+
+            r_np = np.array(r_str, dtype=np.object_)
+            out_tensor = pb_utils.Tensor(self.output_names[0], r_np)
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[out_tensor, ])
+            responses.append(inference_response)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

deploy/fastdeploy/serving/models/postprocess/config.pbtxt

+name: "postprocess"
+backend: "python"
+max_batch_size: 16
+
+input [
+  {
+    name: "post_input"
+    data_type: TYPE_FP32
+    dims: [ 1000 ]
+  }
+]
+
+output [
+  {
+    name: "post_output"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+instance_group [
+  {
+      count: 1
+      kind: KIND_CPU
+  }
+]
\ No newline at end of file

deploy/fastdeploy/serving/models/preprocess/1/inference_cls.yaml

+Global:
+  infer_imgs: "./images/ImageNet/ILSVRC2012_val_00000010.jpeg"
+  inference_model_dir: "./models"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: True
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 0.00392157
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+        channel_num: 3
+    - ToCHWImage:
+
+PostProcess:
+  main_indicator: Topk
+  Topk:
+    topk: 5
+    class_id_map_file: "../ppcls/utils/imagenet1k_label_list.txt"
+  SavePreLabel:
+    save_dir: ./pre_label/

deploy/fastdeploy/serving/models/preprocess/1/model.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import numpy as np
+import os
+
+import fastdeploy as fd
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to intialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        # You must parse model_config. JSON string is not parsed here
+        self.model_config = json.loads(args['model_config'])
+        print("model_config:", self.model_config)
+
+        self.input_names = []
+        for input_config in self.model_config["input"]:
+            self.input_names.append(input_config["name"])
+        print("preprocess input names:", self.input_names)
+
+        self.output_names = []
+        self.output_dtype = []
+        for output_config in self.model_config["output"]:
+            self.output_names.append(output_config["name"])
+            # dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
+            # self.output_dtype.append(dtype)
+            self.output_dtype.append(output_config["data_type"])
+        print("preprocess output names:", self.output_names)
+
+        # init PaddleClasPreprocess class
+        yaml_path = os.path.abspath(os.path.dirname(
+            __file__)) + "/inference_cls.yaml"
+        self.preprocess_ = fd.vision.classification.PaddleClasPreprocessor(
+            yaml_path)
+        if args['model_instance_kind'] == 'GPU':
+            device_id = int(args['model_instance_device_id'])
+            self.preprocess_.use_cuda(False, device_id)
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse.
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        responses = []
+        for request in requests:
+            data = pb_utils.get_input_tensor_by_name(request,
+                                                     self.input_names[0])
+            data = data.as_numpy()
+            outputs = self.preprocess_.run(data)
+
+            # PaddleCls preprocess has only one output
+            dlpack_tensor = outputs[0].to_dlpack()
+            output_tensor = pb_utils.Tensor.from_dlpack(self.output_names[0],
+                                                        dlpack_tensor)
+
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[output_tensor, ])
+            responses.append(inference_response)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

deploy/fastdeploy/serving/models/preprocess/config.pbtxt

+name: "preprocess"
+backend: "python"
+max_batch_size: 16
+
+input [
+  {
+    name: "preprocess_input"
+    data_type: TYPE_UINT8
+    dims: [ -1, -1, 3 ]
+  }
+]
+
+output [
+  {
+    name: "preprocess_output"
+    data_type: TYPE_FP32
+    dims: [ 3, 224, 224 ]
+  }
+]
+
+instance_group [
+  {
+    # The number of instances is 1
+    count: 1
+    # Use CPU, GPU inference option is:KIND_GPU
+    kind: KIND_CPU
+    # The instance is deployed on the 0th GPU card
+    # gpus: [0]
+  }
+]
\ No newline at end of file

deploy/fastdeploy/serving/models/runtime/1/README.md

+# Runtime Directory
+
+This directory holds the model files.
+Paddle models must be model.pdmodel and model.pdiparams files.
+ONNX models must be model.onnx files.

deploy/fastdeploy/serving/models/runtime/config.pbtxt

+# optional, If name is specified it must match the name of the model repository directory containing the model.
+name: "runtime"
+backend: "fastdeploy"
+max_batch_size: 16
+
+# Input configuration of the model
+input [
+  {
+    # input name
+    name: "inputs"
+    # input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
+    data_type: TYPE_FP32
+    # input shape， The batch dimension is omitted and the actual shape is [batch, c, h, w]
+    dims: [ 3, 224, 224 ]
+  }
+]
+
+# The output of the model is configured in the same format as the input
+output [
+  {
+    name: "save_infer_model/scale_0.tmp_1"
+    data_type: TYPE_FP32
+    dims: [ 1000 ]
+  }
+]
+
+# Number of instances of the model
+instance_group [
+  {
+    # The number of instances is 1
+    count: 1
+    # Use GPU, CPU inference option is:KIND_CPU
+    kind: KIND_GPU
+    # The instance is deployed on the 0th GPU card
+    gpus: [0]
+  }
+]
+
+optimization {
+  execution_accelerators {
+  gpu_execution_accelerator : [ {
+    # use TRT engine
+    name: "tensorrt",
+    # use fp16 on TRT engine
+    parameters { key: "precision" value: "trt_fp16" }
+  },
+  {
+    name: "min_shape"
+    parameters { key: "inputs" value: "1 3 224 224" }
+  },
+  {
+    name: "opt_shape"
+    parameters { key: "inputs" value: "1 3 224 224" }
+  },
+  {
+    name: "max_shape"
+    parameters { key: "inputs" value: "16 3 224 224" }
+  }
+  ]
+}}

deploy/fastdeploy/serving/paddlecls_grpc_client.py

+import logging
+import numpy as np
+import time
+from typing import Optional
+import cv2
+import json
+
+from tritonclient import utils as client_utils
+from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput, service_pb2_grpc, service_pb2
+
+LOGGER = logging.getLogger("run_inference_on_triton")
+
+
+class SyncGRPCTritonRunner:
+    DEFAULT_MAX_RESP_WAIT_S = 120
+
+    def __init__(
+            self,
+            server_url: str,
+            model_name: str,
+            model_version: str,
+            *,
+            verbose=False,
+            resp_wait_s: Optional[float]=None, ):
+        self._server_url = server_url
+        self._model_name = model_name
+        self._model_version = model_version
+        self._verbose = verbose
+        self._response_wait_t = self.DEFAULT_MAX_RESP_WAIT_S if resp_wait_s is None else resp_wait_s
+
+        self._client = InferenceServerClient(
+            self._server_url, verbose=self._verbose)
+        error = self._verify_triton_state(self._client)
+        if error:
+            raise RuntimeError(
+                f"Could not communicate to Triton Server: {error}")
+
+        LOGGER.debug(
+            f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} "
+            f"are up and ready!")
+
+        model_config = self._client.get_model_config(self._model_name,
+                                                     self._model_version)
+        model_metadata = self._client.get_model_metadata(self._model_name,
+                                                         self._model_version)
+        LOGGER.info(f"Model config {model_config}")
+        LOGGER.info(f"Model metadata {model_metadata}")
+
+        for tm in model_metadata.inputs:
+            print("tm:", tm)
+        self._inputs = {tm.name: tm for tm in model_metadata.inputs}
+        self._input_names = list(self._inputs)
+        self._outputs = {tm.name: tm for tm in model_metadata.outputs}
+        self._output_names = list(self._outputs)
+        self._outputs_req = [
+            InferRequestedOutput(name) for name in self._outputs
+        ]
+
+    def Run(self, inputs):
+        """
+        Args:
+            inputs: list, Each value corresponds to an input name of self._input_names
+        Returns:
+            results: dict, {name : numpy.array}
+        """
+        infer_inputs = []
+        for idx, data in enumerate(inputs):
+            infer_input = InferInput(self._input_names[idx], data.shape,
+                                     "UINT8")
+            infer_input.set_data_from_numpy(data)
+            infer_inputs.append(infer_input)
+
+        results = self._client.infer(
+            model_name=self._model_name,
+            model_version=self._model_version,
+            inputs=infer_inputs,
+            outputs=self._outputs_req,
+            client_timeout=self._response_wait_t, )
+        results = {name: results.as_numpy(name) for name in self._output_names}
+        return results
+
+    def _verify_triton_state(self, triton_client):
+        if not triton_client.is_server_live():
+            return f"Triton server {self._server_url} is not live"
+        elif not triton_client.is_server_ready():
+            return f"Triton server {self._server_url} is not ready"
+        elif not triton_client.is_model_ready(self._model_name,
+                                              self._model_version):
+            return f"Model {self._model_name}:{self._model_version} is not ready"
+        return None
+
+
+if __name__ == "__main__":
+    model_name = "paddlecls"
+    model_version = "1"
+    url = "localhost:8001"
+    runner = SyncGRPCTritonRunner(url, model_name, model_version)
+    im = cv2.imread("ILSVRC2012_val_00000010.jpeg")
+    im = np.array([im, ])
+    # batch input
+    # im = np.array([im, im, im])
+    for i in range(1):
+        result = runner.Run([im, ])
+        for name, values in result.items():
+            print("output_name:", name)
+            # values is batch
+            for value in values:
+                value = json.loads(value)
+                print(value)

deploy/fastdeploy/sophgo/README.md

+../../../docs/zh_CN/fastdeploy/sophgo/README.md
\ No newline at end of file

deploy/fastdeploy/sophgo/cpp/CMakeLists.txt

+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+set(ENABLE_LITE_BACKEND OFF)
+#set(FDLIB ${FASTDEPLOY_INSTALL_DIR})
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+include_directories(${FastDeploy_INCLUDE_DIRS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

deploy/fastdeploy/sophgo/cpp/README.md

+../../../../docs/zh_CN/fastdeploy/sophgo/cpp/README.md
\ No newline at end of file

deploy/fastdeploy/sophgo/cpp/infer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include "fastdeploy/vision.h"
+#include <string>
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &model_dir, const std::string &image_file) {
+  auto model_file = model_dir + sep + "resnet50_1684x_f32.bmodel";
+  auto params_file = model_dir + sep + "";
+  auto config_file = model_dir + sep + "preprocess_config.yaml";
+
+  fastdeploy::RuntimeOption option;
+  option.UseSophgo();
+  auto model_format = fastdeploy::ModelFormat::SOPHGO;
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option, model_format);
+
+  assert(model.Initialized());
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 3) {
+    std::cout << "Usage: infer_demo path/to/model "
+                 "path/to/image "
+                 "run_option, "
+                 "e.g ./infer_demo ./bmodel ./test.jpeg"
+              << std::endl;
+    return -1;
+  }
+
+  std::string model_dir = argv[1];
+  std::string test_image = argv[2];
+  InitAndInfer(model_dir, test_image);
+  return 0;
+}

deploy/fastdeploy/sophgo/python/README.md

+../../../../docs/zh_CN/fastdeploy/sophgo/python/README.md
\ No newline at end of file

deploy/fastdeploy/sophgo/python/infer.py

+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=True, help="Path of model.")
+    parser.add_argument(
+        "--config_file", required=True, help="Path of config file.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+
+    return parser.parse_args()
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = fd.RuntimeOption()
+runtime_option.use_sophgo()
+
+model_file = args.model
+params_file = ""
+config_file = args.config_file
+
+model = fd.vision.classification.PaddleClasModel(
+    model_file,
+    params_file,
+    config_file,
+    runtime_option=runtime_option,
+    model_format=fd.ModelFormat.SOPHGO)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)

deploy/fastdeploy/web/README.md

+../../../docs/zh_CN/fastdeploy/web/README.md
\ No newline at end of file

docs/zh_CN/fastdeploy/README.md

+# PaddleClas高性能全场景模型部署方案—FastDeploy
+
+## 目录  
+- [FastDeploy介绍](#FastDeploy介绍)  
+- [图像分类模型部署](#图像分类模型部署)  
+- [常见问题](#常见问题)  
+
+## 1. FastDeploy介绍
+<div id="FastDeploy介绍"></div>  
+
+**[⚡️FastDeploy](https://github.com/PaddlePaddle/FastDeploy)**是一款**全场景**、**易用灵活**、**极致高效**的AI推理部署工具，支持**云边端**部署。使用FastDeploy可以简单高效的在X86 CPU、NVIDIA GPU、飞腾CPU、ARM CPU、Intel GPU、Graphcore IPU、昆仑、昇腾、瑞芯微、晶晨、算能等10+款硬件上对PaddleClas模型进行快速部署，并且支持Paddle Inference、Paddle Lite、TensorRT、OpenVINO、ONNXRuntime、RKNPU2、SOPHGO等多种推理后端。
+
+<div align="center">
+
+<img src="https://user-images.githubusercontent.com/31974251/224941235-d5ea4ed0-7626-4c62-8bbd-8e4fad1e72ad.png" >
+
+</div>  
+
+## 2. 图像分类模型部署  
+<div id="图像分类模型部署"></div>  
+
+### 2.1 硬件支持列表
+
+|硬件类型|该硬件是否支持|使用指南|Python|C++|
+|:---:|:---:|:---:|:---:|:---:|
+|X86 CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|NVIDIA GPU|✅|[链接](./cpu-gpu)|✅|✅|
+|飞腾CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|ARM CPU|✅|[链接](./cpu-gpu)|✅|✅|
+|Intel GPU(集成显卡)|✅|[链接](./cpu-gpu)|✅|✅|  
+|Intel GPU(独立显卡)|✅|[链接](./cpu-gpu)|✅|✅|
+|Graphcore IPU|✅|[链接](./graphcore)|✅|✅|
+|昆仑|✅|[链接](./kunlun)|✅|✅|
+|昇腾|✅|[链接](./ascend)|✅|✅|
+|瑞芯微|✅|[链接](./rockchip)|✅|✅|  
+|晶晨|✅|[链接](./amlogic)|--|✅|✅|  
+|算能|✅|[链接](./sophgo)|✅|✅|
+
+
+### 2.2. 详细使用文档
+- X86 CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- NVIDIA GPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- 飞腾CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- ARM CPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- Intel GPU
+  - [部署模型准备](./cpu-gpu)  
+  - [Python部署示例](./cpu-gpu/python/)
+  - [C++部署示例](./cpu-gpu/cpp/)
+- Graphcore IPU
+  - [部署模型准备](./graphcore)  
+  - [Python部署示例](./graphcore/python/)
+  - [C++部署示例](./graphcore/cpp/)
+- 昆仑 XPU
+  - [部署模型准备](./kunlun)  
+  - [Python部署示例](./kunlun/python/)
+  - [C++部署示例](./kunlun/cpp/)
+- 昇腾 Ascend
+  - [部署模型准备](./ascend)  
+  - [Python部署示例](./ascend/python/)
+  - [C++部署示例](./ascend/cpp/)
+- 瑞芯微 Rockchip
+  - [部署模型准备](./rockchip/)  
+  - [Python部署示例](./rockchip/rknpu2/)
+  - [C++部署示例](./rockchip/rknpu2/)
+- 晶晨 Amlogic
+  - [部署模型准备](./amlogic/a311d/)  
+  - [C++部署示例](./amlogic/a311d/cpp/)  
+- 算能 Sophgo
+  - [部署模型准备](./sophgo/)  
+  - [Python部署示例](./sophgo/python/)
+  - [C++部署示例](./sophgo/cpp/)  
+
+### 2.3 更多部署方式
+
+- [Android ARM CPU部署](./android)  
+- [服务化Serving部署](./serving)  
+- [web部署](./web)  
+- [模型自动化压缩工具](./quantize)
+
+
+## 4. 常见问题
+<div id="常见问题"></div>  
+
+遇到问题可查看常见问题集合，搜索FastDeploy issue，*或给FastDeploy提交[issue](https://github.com/PaddlePaddle/FastDeploy/issues)*:
+
+[常见问题集合](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq)  
+[FastDeploy issues](https://github.com/PaddlePaddle/FastDeploy/issues)  

docs/zh_CN/fastdeploy/amlogic/a311d/README.md

+# PaddleClas 模型在晶晨NPU上的部署方案-FastDeploy
+
+## 1. 说明  
+
+晶晨A311D是一款先进的AI应用处理器。PaddleClas支持通过FastDeploy在A311D上基于Paddle-Lite部署相关PaddleClas模型。**注意**：需要注意的是，芯原（verisilicon）作为 IP 设计厂商，本身并不提供实体SoC产品，而是授权其 IP 给芯片厂商，如：晶晨（Amlogic），瑞芯微（Rockchip）等。因此本文是适用于被芯原授权了 NPU IP 的芯片产品。只要芯片产品没有大副修改芯原的底层库，则该芯片就可以使用本文档作为 Paddle Lite 推理部署的参考和教程。在本文中，晶晨 SoC 中的 NPU 和 瑞芯微 SoC 中的 NPU 统称为芯原 NPU。目前支持如下芯片的部署：
+- Amlogic A311D
+- Amlogic C308X
+- Amlogic S905D3
+
+本示例基于晶晨A311D来介绍如何使用FastDeploy部署PaddleClas的量化模型。
+
+## 2. 使用预导出的模型列表  
+
+FastDeploy提供预先量化好的模型进行部署. 更多模型, 欢迎用户参考[FastDeploy 一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/tree/develop/tools/common_tools/auto_compression) 来实现模型量化, 并完成部署.
+
+
+| 模型            | 量化方式 |
+|:---------------| :----- |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar)            | 离线量化 |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/mobilenetv1_ssld_ptq.tar)  | 离线量化 |
+
+## 3. 详细部署示例
+
+目前，A311D上只支持C++的部署。
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/amlogic/a311d/cpp/README.md

+# PaddleClas A311D 开发板 C++ 部署示例
+本目录下提供的 `infer.cc`，可以帮助用户快速完成 PaddleClas 量化模型在 A311D 上的部署推理加速。
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤
+- 1. 在部署前，需自行编译基于A311D的预测库，参考文档[A311D部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+
+## 2. 量化模型准备
+1. 需要特别注意的是，在 A311D 上部署的模型需要是量化后的模型. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
+2. 用户也可以使用 FastDeploy 提供的[一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/tree/develop/tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从 FP32 模型文件夹下复制此 yaml 文件到量化后的模型文件夹内即可.)
+
+## 3. 在 A311D 上部署量化后的 ResNet50_Vd 分类模型
+请按照以下步骤完成在 A311D 上部署 ResNet50_Vd 量化模型：
+1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/a311d.md)
+
+2. 将编译后的库拷贝到当前目录，可使用如下命令：
+```bash
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/amlogic/a311d/cpp/
+cp -r FastDeploy/build/fastdeploy-timvx/ ./
+```
+
+3. 在当前路径下载部署所需的模型和示例图片：
+```bash
+mkdir models && mkdir images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
+tar -xvf resnet50_vd_ptq.tar
+cp -r resnet50_vd_ptq models
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+cp -r ILSVRC2012_val_00000010.jpeg images
+```
+
+4. 编译部署示例，可使入如下命令：
+```bash
+mkdir build && cd build
+cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=arm64 ..
+make -j8
+make install
+# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
+```
+
+5. 基于 adb 工具部署 ResNet50 分类模型到晶晨 A311D，可使用如下命令：
+```bash
+# 进入 install 目录
+cd build/install/
+# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
+bash run_with_adb.sh infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg $DEVICE_ID
+```
+
+部署成功后运行结果如下：
+
+<img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">

docs/zh_CN/fastdeploy/android/README.md

+# PaddleClas Android Demo 使用文档
+
+在 Android 上实现实时的PaddleClas图像分类功能，此 Demo 有很好的的易用性和开放性，如在 Demo 中跑自己训练好的模型等。
+
+## 环境准备
+
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
+
+## 部署步骤
+
+1. 用 Android Studio 打开 paddleclas/android 工程
+2. 手机连接电脑，打开 USB 调试和文件传输模式，并在 Android Studio 上连接自己的手机设备（手机需要开启允许从 USB 安装软件权限）
+
+<p align="center">
+<img width="1280" alt="image" src="https://user-images.githubusercontent.com/31974251/197338597-2c9e1cf0-569b-49b9-a7fb-cdec71921af8.png">
+</p>
+
+> **注意：**
+>> 如果您在导入项目、编译或者运行过程中遇到 NDK 配置错误的提示，请打开 ` File > Project Structure > SDK Location`，修改 `Andriod SDK location` 为您本机配置的 SDK 所在路径。
+
+4. 点击 Run 按钮，自动编译 APP 并安装到手机。(该过程会自动下载预编译的 FastDeploy Android 库，需要联网)
+成功后效果如下，图一：APP 安装到手机；图二： APP 打开后的效果，会自动识别图片中的物体并标记；图三：APP设置选项，点击右上角的设置图片，可以设置不同选项进行体验。
+
+  | APP 图标 | APP 效果 | APP设置项
+  | ---     | --- | --- |
+  | ![app_pic ](https://user-images.githubusercontent.com/14995488/203484427-83de2316-fd60-4baf-93b6-3755f9b5559d.jpg)   | ![app_res](https://user-images.githubusercontent.com/14995488/203494666-16528cb3-0ce2-48fc-9f9e-37da17b2c2f6.jpg) |  ![app_setup](https://user-images.githubusercontent.com/14995488/203484436-57fdd041-7dcc-4e0e-b6cb-43e5ac1e729b.jpg) |
+
+## PaddleClasModel Java API 说明  
+- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleClasModel初始化参数说明如下：  
+  - modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
+  - paramFile: String, paddle格式的参数文件路径，如 model.pdiparams  
+  - configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml  
+  - labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 imagenet1k_label_list.txt，每一行包含一个label  
+  - option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。  
+
+```java
+// 构造函数: constructor w/o label file
+public PaddleClasModel(); // 空构造函数，之后可以调用init初始化
+public PaddleClasModel(String modelFile, String paramsFile, String configFile);
+public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile);
+public PaddleClasModel(String modelFile, String paramsFile, String configFile, RuntimeOption option);
+public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
+// 手动调用init初始化: call init manually w/o label file
+public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
+public boolean init(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
+```  
+- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
+```java
+// 直接预测：不保存图片以及不渲染结果到Bitmap上
+public ClassifyResult predict(Bitmap ARGB8888Bitmap)；
+// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
+public ClassifyResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float scoreThreshold)
+```
+- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
+```java
+public boolean release(); // 释放native资源  
+public boolean initialized(); // 检查是否初始化成功
+```
+- RuntimeOption设置说明  
+```java  
+public void enableLiteFp16(); // 开启fp16精度推理
+public void disableLiteFP16(); // 关闭fp16精度推理
+public void setCpuThreadNum(int threadNum); // 设置线程数
+public void setLitePowerMode(LitePowerMode mode);  // 设置能耗模式
+public void setLitePowerMode(String modeStr);  // 通过字符串形式设置能耗模式
+public void enableRecordTimeOfRuntime();  // 是否打印模型运行耗时
+```
+
+- 模型结果ClassifyResult说明  
+```java
+public float[] mScores;  // [n]   得分
+public int[] mLabelIds;  // [n]   分类ID
+public boolean initialized(); // 检测结果是否有效
+```  
+
+- 模型调用示例1：使用构造函数以及默认的RuntimeOption
+```java  
+import java.nio.ByteBuffer;
+import android.graphics.Bitmap;
+import android.opengl.GLES20;
+
+import com.baidu.paddle.fastdeploy.vision.ClassifyResult;
+import com.baidu.paddle.fastdeploy.vision.classification.PaddleClasModel;
+
+// 初始化模型
+PaddleClasModel model = new PaddleClasModel("MobileNetV1_x0_25_infer/inference.pdmodel",
+                                            "MobileNetV1_x0_25_infer/inference.pdiparams",
+                                            "MobileNetV1_x0_25_infer/inference_cls.yml");
+
+// 读取图片: 以下仅为读取Bitmap的伪代码
+ByteBuffer pixelBuffer = ByteBuffer.allocate(width * height * 4);
+GLES20.glReadPixels(0, 0, width, height, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, pixelBuffer);
+Bitmap ARGB8888ImageBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
+ARGB8888ImageBitmap.copyPixelsFromBuffer(pixelBuffer);
+
+// 模型推理
+ClassifyResult result = model.predict(ARGB8888ImageBitmap);  
+
+// 释放模型资源  
+model.release();
+```  
+
+- 模型调用示例2: 在合适的程序节点，手动调用init，并自定义RuntimeOption
+```java  
+// import 同上 ...
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.LitePowerMode;
+import com.baidu.paddle.fastdeploy.vision.ClassifyResult;
+import com.baidu.paddle.fastdeploy.vision.classification.PaddleClasModel;
+// 新建空模型
+PaddleClasModel model = new PaddleClasModel();  
+// 模型路径
+String modelFile = "MobileNetV1_x0_25_infer/inference.pdmodel";
+String paramFile = "MobileNetV1_x0_25_infer/inference.pdiparams";
+String configFile = "MobileNetV1_x0_25_infer/inference_cls.yml";
+// 指定RuntimeOption
+RuntimeOption option = new RuntimeOption();
+option.setCpuThreadNum(2);
+option.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+option.enableRecordTimeOfRuntime();
+option.enableLiteFp16();
+// 使用init函数初始化  
+model.init(modelFile, paramFile, configFile, option);
+// Bitmap读取、模型预测、资源释放 同上 ...
+```
+更详细的用法请参考 [MainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/classification/ClassificationMainActivity.java) 中的用法
+
+## 替换 FastDeploy 预测库和模型  
+替换FastDeploy预测库和模型的步骤非常简单。预测库所在的位置为 `app/libs/fastdeploy-android-xxx-shared`，其中 `xxx` 表示当前您使用的预测库版本号。模型所在的位置为，`app/src/main/assets/models/MobileNetV1_x0_25_infer`。  
+- 替换FastDeploy预测库的步骤:
+  - 下载或编译最新的FastDeploy Android预测库，解压缩后放在 `app/libs` 目录下;  
+  - 修改 `app/src/main/cpp/CMakeLists.txt` 中的预测库路径，指向您下载或编译的预测库路径。如：  
+```cmake  
+set(FastDeploy_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../libs/fastdeploy-android-xxx-shared")
+```
+- 替换PaddleClas模型的步骤：
+  - 将您的PaddleClas分类模型放在 `app/src/main/assets/models` 目录下；  
+  - 修改 `app/src/main/res/values/strings.xml` 中模型路径的默认值，如：  
+```xml
+<!-- 将这个路径指修改成您的模型，如 models/MobileNetV2_x0_25_infer -->
+<string name="CLASSIFICATION_MODEL_DIR_DEFAULT">models/MobileNetV1_x0_25_infer</string>  
+<string name="CLASSIFICATION_LABEL_PATH_DEFAULT">labels/imagenet1k_label_list.txt</string>
+```  
+
+## 更多参考文档
+如果您想知道更多的FastDeploy Java API文档以及如何通过JNI来接入FastDeploy C++ API感兴趣，可以参考以下内容:
+- [在 Android 中使用 FastDeploy Java SDK](https://github.com/PaddlePaddle/FastDeploy/tree/develop/java/android)
+- [在 Android 中使用 FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_cpp_sdk_on_android.md)  

docs/zh_CN/fastdeploy/ascend/README.md

+[English](README.md) | 简体中文
+
+# PaddleClas 模型在昇腾上的部署方案-FastDeploy
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在昇腾上部署图像分类模型.
+
+## 2. 模型版本说明
+
+- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
+
+目前FastDeploy支持如下模型的部署
+
+- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
+- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
+- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
+- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
+- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
+- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+
+### 2.1 准备PaddleClas部署模型
+
+PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+
+注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
+
+
+### 2.2 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+
+| 模型                                                               | 参数文件大小    |输入Shape |  Top1 | Top5 |
+|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
+| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
+| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz)  | 26MB  | 224x224 |77.04% | 93.27% |
+| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) |  255MB | 600x600 | 84.3% | 96.9% |
+| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)|  18MB | 224x224 | 75.8% | 75.8% |
+| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) |  29MB | 224x224 | 75.7% | 92.5% |
+| [GhostNet_x0_5](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) |  10MB | 224x224 | 66.8% | 86.9% |
+| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) |  1.9MB | 224x224 | 51.4% | 75.5% |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) |  17MB | 224x224 | 77.9% | 93.9% |
+| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) |  5.9MB | 224x224 | 53.2% | 76.5% |
+| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) |  14MB | 224x224 | 76.74% | 93.39% |
+| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) |  6.4MB | 224x224 | 55.55% | 77.71% |
+| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) |  22MB | 224x224 | 78.96% | 94.48% |
+| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) |  2.4MB | 224x224 | 49.9% | 73.79% |
+| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) |  29MB | 224x224 | 73.15% | 91.2% |
+| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) |  4.8MB | 224x224 | 60.1% | 81.9% |
+| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) |  92MB | 299x299 | 79.14% | 94.59% |
+| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) |  57MB | 224x224 | 81.95% | 96.12% |
+| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) |  274MB | 224x224 | 85.0% | 97.35% |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) |  98MB | 224x224 | 79.12% | 94.44% |
+
+
+## 3. 详细部署的部署示例  
+- [Python部署](python)
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/ascend/cpp/README.md

+# PaddleClas 昇腾 C++部署示例
+
+本目录下提供`infer.cc`, 供用户完成PaddleClas模型在昇腾AI处理器上的部署.
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤
+- 1. 在部署前，需自行编译基于昇腾AI处理器的预测库，参考文档[昇腾AI处理器部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+- 2. 部署时需要环境初始化, 请参考[如何使用C++在昇腾AI处理器部署](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_ascend.md)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/ascend/cpp
+
+mkdir build
+cd build
+# 使用编译完成的FastDeploy库编译infer_demo
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-ascend
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 使用昇腾部署
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg
+
+```
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas Python部署](../python)

docs/zh_CN/fastdeploy/ascend/python/README.md

+# PaddleClas 昇腾 Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在昇腾AI处理器上部署的示例.
+
+## 1. 部署环境准备
+在部署前，需自行编译基于昇腾AI处理器的FastDeploy python wheel包并安装，参考文档，参考文档[昇腾AI处理器部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+```bash
+# 安装FastDpeloy 昇腾预测库 python包（详细文档请参考`部署环境准备`）
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/ascend/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在Ascend AI 处理器上推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas C++ 部署](../cpp)

docs/zh_CN/fastdeploy/cpu-gpu/README.md

+[English](README.md) | 简体中文
+
+# PaddleClas 模型在CPU与GPU上的部署方案-FastDeploy
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署PaddleClas系列模型
+
+## 2. 模型版本说明
+
+- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
+
+目前FastDeploy支持如下模型的部署
+
+- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
+- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
+- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
+- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
+- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
+- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+
+### 2.1 准备PaddleClas部署模型
+
+PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+
+注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
+
+
+### 2.2 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+
+| 模型                                                               | 参数文件大小    |输入Shape |  Top1 | Top5 |
+|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
+| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
+| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz)  | 26MB  | 224x224 |77.04% | 93.27% |
+| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) |  255MB | 600x600 | 84.3% | 96.9% |
+| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)|  18MB | 224x224 | 75.8% | 75.8% |
+| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) |  29MB | 224x224 | 75.7% | 92.5% |
+| [GhostNet_x0_5](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) |  10MB | 224x224 | 66.8% | 86.9% |
+| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) |  1.9MB | 224x224 | 51.4% | 75.5% |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) |  17MB | 224x224 | 77.9% | 93.9% |
+| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) |  5.9MB | 224x224 | 53.2% | 76.5% |
+| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) |  14MB | 224x224 | 76.74% | 93.39% |
+| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) |  6.4MB | 224x224 | 55.55% | 77.71% |
+| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) |  22MB | 224x224 | 78.96% | 94.48% |
+| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) |  2.4MB | 224x224 | 49.9% | 73.79% |
+| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) |  29MB | 224x224 | 73.15% | 91.2% |
+| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) |  4.8MB | 224x224 | 60.1% | 81.9% |
+| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) |  92MB | 299x299 | 79.14% | 94.59% |
+| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) |  57MB | 224x224 | 81.95% | 96.12% |
+| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) |  274MB | 224x224 | 85.0% | 97.35% |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) |  98MB | 224x224 | 79.12% | 94.44% |
+
+
+## 3. 详细部署的部署示例  
+- [Python部署](python)
+- [C++部署](cpp)
+- [C部署](c)
+- [C#部署](csharp)

docs/zh_CN/fastdeploy/cpu-gpu/c/README.md

+[English](README.md) | 简体中文
+# PaddleClas CPU-GPU C部署示例
+
+本目录下提供`infer.c`来调用C API快速完成PaddleClas模型在CPU/GPU上部署的示例。
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型.
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4.运行部署示例
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/cpu-gpu/c
+
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# 使用CPU在OpenVINO推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# 使用GPU在TensorRT推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+```
+
+- 注意，以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考文档: [如何在Windows中使用FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows.md)  
+
+
+## 5. PaddleClas C API接口简介
+下面提供了PaddleClas的C API简介
+- 如果用户想要更换部署后端或进行其他定制化操作, 请查看[C Runtime API](https://baidu-paddle.github.io/fastdeploy-api/c/html/runtime__option_8h.html).
+- 更多 PaddleClas C API 请查看 [C PaddleClas API](https://github.com/PaddlePaddle/FastDeploy/blob/develop/c_api/fastdeploy_capi/vision/classification/ppcls/model.h)
+
+### 配置
+
+```c
+FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
+```
+
+> 创建一个RuntimeOption的配置对象，并且返回操作它的指针。
+>
+> **返回**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+
+
+```c
+void FD_C_RuntimeOptionWrapperUseCpu(
+     FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
+```
+
+> 开启CPU推理
+>
+> **参数**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+
+```c
+void FD_C_RuntimeOptionWrapperUseGpu(
+    FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
+    int gpu_id)
+```
+> 开启GPU推理
+>
+> **参数**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+> * **gpu_id**(int): 显卡号
+
+
+### 模型
+
+```c
+
+FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
+    const char* model_file, const char* params_file, const char* config_file,
+    FD_C_RuntimeOptionWrapper* runtime_option,
+    const FD_C_ModelFormat model_format)
+
+```
+
+> 创建一个PaddleClas的模型，并且返回操作它的指针。
+>
+> **参数**
+>
+> * **model_file**(const char*): 模型文件路径
+> * **params_file**(const char*): 参数文件路径
+> * **config_file**(const char*): 配置文件路径，即PaddleClas导出的部署yaml文件
+> * **runtime_option**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption的指针，表示后端推理配置
+> * **model_format**(FD_C_ModelFormat): 模型格式
+>
+> **返回**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型对象的指针
+
+
+### 读写图像
+
+```c
+FD_C_Mat FD_C_Imread(const char* imgpath)
+```
+
+> 读取一个图像，并且返回cv::Mat的指针。
+>
+> **参数**
+>
+> * **imgpath**(const char*): 图像文件路径
+>
+> **返回**
+>
+> * **imgmat**(FD_C_Mat): 指向图像数据cv::Mat的指针。
+
+
+```c
+FD_C_Bool FD_C_Imwrite(const char* savepath,  FD_C_Mat img);
+```
+
+> 将图像写入文件中。
+>
+> **参数**
+>
+> * **savepath**(const char*): 保存图像的路径
+> * **img**(FD_C_Mat): 指向图像数据的指针
+>
+> **返回**
+>
+> * **result**(FD_C_Bool): 表示操作是否成功
+
+
+### Predict函数
+
+```c
+FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
+    __fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
+    FD_C_ClassifyResult* fd_c_ppclas_result)
+```
+>
+> 模型预测接口，输入图像直接并生成分类结果。
+>
+> **参数**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型的指针
+> * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取
+> * **fd_c_ppclas_result**（FD_C_ClassifyResult*): 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+
+### Predict结果
+
+```c
+void FD_C_ClassifyResultStr(
+    FD_C_ClassifyResult* fd_c_classify_result， char* str_buffer);
+```
+>
+> 打印结果
+>
+> **参数**
+> * **fd_c_classify_result**(FD_C_ClassifyResult*): 指向FD_C_ClassifyResult对象的指针
+> * **str_buffer**(char*): 保存结果数据信息的字符串
+
+
+## 6. 其它文档
+
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas Python部署](../python)
+- [PaddleClas C++ 部署](../cpp)
+- [PaddleClas C# 部署](../csharp)

docs/zh_CN/fastdeploy/cpu-gpu/cpp/README.md

+# PaddleClas CPU-GPU C++部署示例
+
+本目录下提供`infer.cc`快速完成PaddleClas系列模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型.
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4. 运行部署示例
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/cpu-gpu/cpp
+
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# 在CPU上使用Paddle Inference推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# 在CPU上使用OenVINO推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+# 在CPU上使用ONNX Runtime推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
+# 在CPU上使用Paddle Lite推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 3
+# 在GPU上使用Paddle Inference推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 4
+# 在GPU上使用Paddle TensorRT推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 5
+# 在GPU上使用ONNX Runtime推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 6
+# 在GPU上使用Nvidia TensorRT推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 7
+```
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
+- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
+
+
+## 5. 部署示例选项说明  
+在我们使用`infer_demo`时, 输入了3个参数, 分别为分类模型, 预测图片, 与最后一位的数字选项.
+现在下表将解释最后一位数字选项的含义.
+|数字选项|含义|
+|:---:|:---:|
+|0| 在CPU上使用Paddle Inference推理 |
+|1| 在CPU上使用OenVINO推理 |
+|2| 在CPU上使用ONNX Runtime推理 |
+|3| 在CPU上使用Paddle Lite推理 |
+|4| 在GPU上使用Paddle Inference推理 |
+|5| 在GPU上使用Paddle TensorRT推理 |
+|6| 在GPU上使用ONNX Runtime推理 |
+|7| 在GPU上使用Nvidia TensorRT推理 |
+
+- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+## 6. 更多指南
+- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
+- [PaddleClas Python部署](../python)
+- [PaddleClas C 部署](../c)
+- [PaddleClas C# 部署](../csharp)
+
+## 7. 常见问题
+- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+|硬件类型|支持的后端|
+|:---:|:---:|
+|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
+|ARM CPU| Paddle Lite |
+|飞腾 CPU| ONNX Runtime |
+|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
+
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)

docs/zh_CN/fastdeploy/cpu-gpu/csharp/README.md

+# PaddleClas CPU-GPU C#部署示例
+
+本目录下提供`infer.cs`来调用C# API快速完成PaddleClas模型在CPU/GPU上部署的示例。
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型.
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库. 在本目录执行如下命令即可在Windows完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4. 部署示例
+
+### 4.1 下载C#包管理程序nuget客户端
+> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
+
+下载完成后将该程序添加到环境变量**PATH**中
+
+### 4.2 下载模型文件和测试图片
+> https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz # (下载后解压缩)
+> https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+### 4.3 编译示例代码
+
+本文档编译的示例代码的编译工具依赖VS 2019，**Windows打开x64 Native Tools Command Prompt for VS 2019命令工具**，通过如下命令开始编译
+
+```shell
+## 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd D:\PaddleClas\deploy\fastdeploy\cpu-gpu\csharp
+
+mkdir build && cd build
+cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
+
+nuget restore
+msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
+```
+
+关于使用Visual Studio 2019创建sln工程，或者CMake工程等方式编译的更详细信息，可参考如下文档
+- [在 Windows 使用 FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq/use_sdk_on_windows.md)
+- [FastDeploy C++库在Windows上的多种使用方式](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq/use_sdk_on_windows_build.md)
+
+## 4.4 运行可执行程序
+
+注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录(可能生成的可执行文件在Release下还有一层目录，这里假设生成的可执行文件在Release处)
+```shell
+cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
+
+fastdeploy_init.bat install %cd% D:\PaddleClas\deploy\fastdeploy\cpu-gpu\csharp\build\Release
+```
+
+将dll拷贝到当前路径后，准备好模型和图片，使用如下命令运行可执行程序即可
+```shell
+cd Release
+# CPU推理
+infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# GPU推理
+infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+```
+
+## 5. PaddleClas C#接口简介
+
+下面提供了PaddleClas的C# API简介
+
+- 如果用户想要更换部署后端或进行其他定制化操作, 请查看[C# Runtime API](https://github.com/PaddlePaddle/FastDeploy/blob/develop/csharp/fastdeploy/runtime_option.cs).
+- 更多 PaddleClas C# API 请查看 [C# PaddleClas API](https://github.com/PaddlePaddle/FastDeploy/blob/develop/csharp/fastdeploy/vision/classification/ppcls/model.cs)
+
+### 模型
+
+```c#
+fastdeploy.vision.classification.PaddleClasModel(
+        string model_file,
+        string params_file,
+        string config_file,
+        fastdeploy.RuntimeOption runtime_option = null,
+        fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
+```
+
+> PaddleClasModel模型加载和初始化。
+
+> **参数**
+
+>> * **model_file**(str): 模型文件路径
+>> * **params_file**(str): 参数文件路径
+>> * **config_file**(str): 配置文件路径，即PaddleClas导出的部署yaml文件
+>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，即采用默认配置
+>> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
+
+### Predict函数
+
+```c#
+fastdeploy.ClassifyResult Predict(OpenCvSharp.Mat im)
+```
+
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+>> * **im**(Mat): 输入图像，注意需为HWC，BGR格式
+>>
+> **返回值**
+>
+>> * **result**: 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+
+## 6. 其它文档
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas Python部署](../python)
+- [PaddleClas C++ 部署](../cpp)
+- [PaddleClas C 部署](../c)

docs/zh_CN/fastdeploy/cpu-gpu/python/README.md

+# PaddleClas CPU-GPU Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在CPU/GPU上部署的示例.
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4. 运行部署示例
+```bash
+# 安装FastDpeloy python包（详细文档请参考`部署环境准备`）
+pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
+conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/cpu-gpu/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在CPU上使用Paddle Inference推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend paddle --topk 1
+# 在CPU上使用OenVINO推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend openvino --topk 1
+# 在CPU上使用ONNX Runtime推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend ort --topk 1
+# 在CPU上使用Paddle Lite推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend pplite --topk 1
+# 在GPU上使用Paddle Inference推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend paddle --topk 1
+# 在GPU上使用Paddle TensorRT推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend pptrt --topk 1
+# 在GPU上使用ONNX Runtime推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend ort --topk 1
+# 在GPU上使用Nvidia TensorRT推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend trt --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 5. 部署示例选项说明  
+
+|参数|含义|默认值
+|---|---|---|  
+|--model|指定模型文件夹所在的路径|None|
+|--image|指定测试图片所在的路径|None|  
+|--device|指定即将运行的硬件类型，支持的值为`[cpu, gpu]`，当设置为cpu时，可运行在x86 cpu/arm cpu等cpu上|cpu|
+|--device_id|使用gpu时, 指定设备号|0|
+|--backend|部署模型时使用的后端, 支持的值为`[paddle,pptrt,pplite,ort,openvino,trt]` |openvino|
+|--topk|返回的前topk准确率, 支持的为`1,5` |1|
+
+关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+## 6. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [PaddleClas C++ 部署](../cpp)
+- [PaddleClas C 部署](../c)
+- [PaddleClas C# 部署](../csharp)
+
+## 7. 常见问题
+- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+|硬件类型|支持的后端|
+|:---:|:---:|
+|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
+|ARM CPU| Paddle Lite |
+|飞腾 CPU| ONNX Runtime |
+|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
+
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)

docs/zh_CN/fastdeploy/graphcore/README.md

+[English](README.md) | 简体中文
+
+# PaddleClas 模型在Graphcore IPU的部署方案-FastDeploy
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在Graphcore IPU上部署图像分类模型.
+
+## 2. 模型版本说明
+
+- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
+
+目前FastDeploy支持如下模型的部署
+
+- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
+- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
+- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
+- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
+- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
+- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+
+### 2.1 准备PaddleClas部署模型
+
+PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+
+注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
+
+
+### 2.2 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+
+| 模型                                                               | 参数文件大小    |输入Shape |  Top1 | Top5 |
+|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
+| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
+| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz)  | 26MB  | 224x224 |77.04% | 93.27% |
+| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) |  255MB | 600x600 | 84.3% | 96.9% |
+| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)|  18MB | 224x224 | 75.8% | 75.8% |
+| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) |  29MB | 224x224 | 75.7% | 92.5% |
+| [GhostNet_x0_5](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) |  10MB | 224x224 | 66.8% | 86.9% |
+| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) |  1.9MB | 224x224 | 51.4% | 75.5% |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) |  17MB | 224x224 | 77.9% | 93.9% |
+| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) |  5.9MB | 224x224 | 53.2% | 76.5% |
+| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) |  14MB | 224x224 | 76.74% | 93.39% |
+| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) |  6.4MB | 224x224 | 55.55% | 77.71% |
+| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) |  22MB | 224x224 | 78.96% | 94.48% |
+| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) |  2.4MB | 224x224 | 49.9% | 73.79% |
+| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) |  29MB | 224x224 | 73.15% | 91.2% |
+| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) |  4.8MB | 224x224 | 60.1% | 81.9% |
+| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) |  92MB | 299x299 | 79.14% | 94.59% |
+| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) |  57MB | 224x224 | 81.95% | 96.12% |
+| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) |  274MB | 224x224 | 85.0% | 97.35% |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) |  98MB | 224x224 | 79.12% | 94.44% |
+
+
+## 3. 详细部署的部署示例  
+- [Python部署](python)
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/graphcore/cpp/README.md

+# PaddleClas Graphcore IPU C++部署示例
+
+本目录下提供`infer.cc`, 供用户完成PaddleClas模型在Graphcore IPU上的部署.
+
+## 1. 部署环境准备
+- 在部署前，需自行编译基于Graphcore IPU的预测库，参考文档[Graphcore IPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/graphcore/cpp
+
+mkdir build
+cd build
+# 使用编译完成的FastDeploy库编译infer_demo
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-graphcore
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 使用IPU部署
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg
+
+```
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas Python部署](../python)

docs/zh_CN/fastdeploy/graphcore/python/README.md

+# PaddleClas Graphcore IPU Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在Graphcore IPU上部署的示例.
+
+## 1. 部署环境准备
+在部署前，需自行编译基于Graphcore IPU的FastDeploy python wheel包并安装，参考文档，参考文档[Graphcore IPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+```bash
+# 安装FastDpeloy Graphcore IPU预测库 python包（详细文档请参考`部署环境准备`）
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/graphcore/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在Graphcore AI 处理器上推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas C++ 部署](../cpp)

docs/zh_CN/fastdeploy/kunlun/README.md

+# PaddleClas 模型在昆仑芯上的部署方案-FastDeploy
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在昆仑芯上部署图像分类模型.
+
+## 2. 模型版本说明
+
+- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
+
+目前FastDeploy支持如下模型的部署
+
+- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
+- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
+- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
+- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
+- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
+- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
+- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+
+### 2.1 准备PaddleClas部署模型
+
+PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)  
+
+注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
+
+
+### 2.2 下载预训练模型
+
+为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
+
+| 模型                                                               | 参数文件大小    |输入Shape |  Top1 | Top5 |
+|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
+| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
+| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz)  | 26MB  | 224x224 |77.04% | 93.27% |
+| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) |  255MB | 600x600 | 84.3% | 96.9% |
+| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)|  18MB | 224x224 | 75.8% | 75.8% |
+| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) |  29MB | 224x224 | 75.7% | 92.5% |
+| [GhostNet_x0_5](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) |  10MB | 224x224 | 66.8% | 86.9% |
+| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) |  1.9MB | 224x224 | 51.4% | 75.5% |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) |  17MB | 224x224 | 77.9% | 93.9% |
+| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) |  5.9MB | 224x224 | 53.2% | 76.5% |
+| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) |  14MB | 224x224 | 76.74% | 93.39% |
+| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) |  6.4MB | 224x224 | 55.55% | 77.71% |
+| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) |  22MB | 224x224 | 78.96% | 94.48% |
+| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) |  2.4MB | 224x224 | 49.9% | 73.79% |
+| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) |  29MB | 224x224 | 73.15% | 91.2% |
+| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) |  4.8MB | 224x224 | 60.1% | 81.9% |
+| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) |  92MB | 299x299 | 79.14% | 94.59% |
+| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) |  57MB | 224x224 | 81.95% | 96.12% |
+| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) |  274MB | 224x224 | 85.0% | 97.35% |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) |  98MB | 224x224 | 79.12% | 94.44% |
+
+
+## 3. 详细部署的部署示例  
+- [Python部署](python)
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/kunlun/cpp/README.md

+# PaddleClas 昆仑芯XPU C++部署示例
+
+本目录下提供`infer.cc`, 供用户完成PaddleClas模型在昆仑芯XPU上的部署.
+
+## 1. 部署环境准备
+在部署前，需自行编译基于昆仑芯XPUXPU的预测库，参考文档[昆仑芯XPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/kunlun/cpp
+
+mkdir build
+cd build
+# 使用编译完成的FastDeploy库编译infer_demo
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-kunlun
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 使用昆仑芯XPU部署
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg
+
+```
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas Python部署](../python)

docs/zh_CN/fastdeploy/kunlun/python/README.md

+# PaddleClas 昆仑芯XPU Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在昆仑芯XPU上部署的示例.
+
+## 1. 部署环境准备
+在部署前，需自行编译基于昆仑芯XPU的FastDeploy python wheel包并安装，参考文档，参考文档[昆仑芯XPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 3. 运行部署示例
+```bash
+# 安装FastDpeloy 预测库 python包（详细文档请参考`部署环境准备`）
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/kunlun/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在昆仑芯XPU AI 处理器上推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 4. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [FastDeploy部署PaddleClas模型概览](../../)
+- [PaddleClas C++ 部署](../cpp)

docs/zh_CN/fastdeploy/quantize/README.md

+
+# PaddleClas 量化模型部署-FastDeploy
+
+FastDeploy已支持部署量化模型,并提供一键模型自动化压缩的工具.
+用户可以使用一键模型自动化压缩工具,自行对模型量化后部署, 也可以直接下载FastDeploy提供的量化模型进行部署.
+
+## 1. FastDeploy一键模型自动化压缩工具  
+
+FastDeploy 提供了一键模型自动化压缩工具, 能够简单地通过输入一个配置文件, 对模型进行量化.
+详细教程请见: [一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/tree/develop/tools/common_tools/auto_compression)。**注意**: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
+
+## 2. 下载量化完成的PaddleClas模型  
+
+用户也可以直接下载下表中的量化模型进行部署.(点击模型名字即可下载)
+
+| 模型                 | 量化方式 |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar)            | 离线量化 |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/mobilenetv1_ssld_ptq.tar)  |  离线量化 |
+
+量化后模型的Benchmark比较，请参考[量化模型 Benchmark](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/quantize.md)
+
+## 3. 部署量化模型
+
+### 3.1 部署代码
+FastDeploy 部署量化模型与部署FP32模型完全一致, 用户只需要将输入的模型换为量化后的模型即可.
+如果硬件在量化模型部署过程有特殊处理，也会在文档中特别标明.
+因此本目录下，不提供代码文件, 量化模型部署参考对应的硬件部署即可, 具体请点击下一小节里的链接.
+
+### 3.2 支持部署量化模型的硬件  
+
+|硬件类型|该硬件是否支持|使用指南|Python|C++|
+|:---:|:---:|:---:|:---:|:---:|
+|X86 CPU|✅|[链接](cpu-gpu)|✅|✅|
+|NVIDIA GPU|✅|[链接](cpu-gpu)|✅|✅|
+|飞腾CPU|✅|[链接](cpu-gpu)|✅|✅|
+|ARM CPU|✅|[链接](cpu-gpu)|✅|✅|
+|Intel GPU(集成显卡)|✅|[链接](cpu-gpu)|✅|✅|  
+|Intel GPU(独立显卡)|✅|[链接](cpu-gpu)|✅|✅|  
+|昆仑|✅|[链接](kunlun)|✅|✅|
+|昇腾|✅|[链接](ascend)|✅|✅|
+|瑞芯微|✅|[链接](rockchip)|✅|✅|  
+|晶晨|✅|[链接](amlogic)|--|✅|  
+|算能|✅|[链接](sophgo)|✅|✅|  

docs/zh_CN/fastdeploy/rockchip/rknpu2/README.md

+
+# PaddleClas 模型在RKNPU2上部署方案-FastDeploy
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在RKNPU2上部署相关模型.
+
+## 2. 转换模型
+下面以 ResNet50_vd为例子，教大家如何转换分类模型到RKNN模型.
+目前FastDeploy只在PaddleClas测试过ResNet50_vd模型, 欢迎用户尝试其他的PaddleClas模型进行部署.
+
+### 2.1 导出ONNX模型
+```bash
+# 安装 paddle2onnx
+pip install paddle2onnx
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+
+# 静态图转ONNX模型，注意，这里的save_file请和压缩包名对齐
+paddle2onnx --model_dir ResNet50_vd_infer  \
+            --model_filename inference.pdmodel \
+            --params_filename inference.pdiparams  \
+            --save_file ResNet50_vd_infer/ResNet50_vd_infer.onnx  \
+            --enable_dev_version True  \
+            --opset_version 10  \
+            --enable_onnx_checker True
+
+# 固定shape，注意这里的inputs得对应netron.app展示的 inputs 的 name，有可能是image 或者 x
+python -m paddle2onnx.optimize --input_model ResNet50_vd_infer/ResNet50_vd_infer.onnx \
+                               --output_model ResNet50_vd_infer/ResNet50_vd_infer.onnx \
+                               --input_shape_dict "{'inputs':[1,3,224,224]}"
+```  
+
+### 2.2 编写模型导出配置文件
+以转化RK3588的RKNN模型为例子，我们需要编辑./rknpu2_tools/config/ResNet50_vd_infer_rknn.yaml，来转换ONNX模型到RKNN模型。
+
+如果你需要在NPU上执行normalize操作，请根据你的模型配置normalize参数，例如:
+```yaml
+model_path: ./ResNet50_vd_infer/ResNet50_vd_infer.onnx
+output_folder: ./ResNet50_vd_infer
+mean:
+  -
+    - 123.675
+    - 116.28
+    - 103.53
+std:
+  -
+    - 58.395
+    - 57.12
+    - 57.375
+outputs_nodes:
+do_quantization: False
+dataset: "./ResNet50_vd_infer/dataset.txt"
+```
+
+**在CPU上做normalize**可以参考以下yaml：
+```yaml
+model_path: ./ResNet50_vd_infer/ResNet50_vd_infer.onnx
+output_folder: ./ResNet50_vd_infer
+mean:
+  -
+    - 0
+    - 0
+    - 0
+std:
+  -
+    - 1
+    - 1
+    - 1
+outputs_nodes:
+do_quantization: False
+dataset: "./ResNet50_vd_infer/dataset.txt"
+```
+这里我们选择在NPU上执行normalize操作.
+
+
+### 2.3 ONNX模型转RKNN模型
+```shell
+python ./rknpu2_tools/export.py \
+        --config_path ./rknpu2_tools/config/ResNet50_vd_infer_rknn.yaml \
+        --target_platform rk3588
+```
+
+## 3. 其他链接
+- [Cpp部署](./cpp)
+- [Python部署](./python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)

docs/zh_CN/fastdeploy/rockchip/rknpu2/cpp/README.md

+# PaddleClas RKNPU2 C++部署示例
+
+本目录下提供`infer.cc`, 供用户完成PaddleClas模型在RKNPU2的部署.
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤
+- 1. 在部署前，需自行编译基于RKNPU2的预测库，参考文档[RKNPU2部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+- 2. 同时请用户参考[FastDeploy RKNPU2资源导航](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md)
+
+## 2.部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以参考[RKNPU2模型转换](../README.md), 来准备模型.
+
+## 3.部署示例
+
+### 3.1 生成基本目录文件
+
+该例程由以下几个部分组成
+```text
+.
+├── CMakeLists.txt
+├── build  # 编译文件夹
+├── images  # 存放图片的文件夹
+├── infer.cc
+├── ppclas_model_dir  # 存放模型文件的文件夹
+└── thirdpartys  # 存放sdk的文件夹
+```
+
+首先需要先生成目录结构
+```bash
+mkdir build
+mkdir images
+mkdir ppclas_model_dir
+mkdir thirdpartys
+```
+
+### 3.2 编译
+
+#### 3.2.1 编译并拷贝SDK到thirdpartys文件夹
+
+请参考[RK2代NPU部署库编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md)仓库编译SDK，编译完成后，将在build目录下生成fastdeploy-x.x.x目录，请移动它至thirdpartys目录下.
+
+#### 3.2.2 拷贝模型文件，以及配置文件至model文件夹
+在Paddle动态图模型 -> Paddle静态图模型 -> ONNX模型的过程中，将生成ONNX文件以及对应的yaml配置文件，请将配置文件存放到model文件夹内。
+转换为RKNN后的模型文件也需要拷贝至model，转换方案: ([ResNet50_vd RKNN模型](../README.md))。
+
+#### 3.2.3 准备测试图片至image文件夹
+```bash
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+```
+
+#### 3.2.4 编译example
+
+```bash
+cd build
+cmake ..
+make -j8
+make install
+```
+
+#### 3.2.5 运行例程
+
+```bash
+cd ./build/install
+./rknpu_test ./ppclas_model_dir ./images/ILSVRC2012_val_00000010.jpeg
+```
+
+#### 3.2.6 运行结果展示
+ClassifyResult(
+label_ids: 153,
+scores: 0.684570,
+)
+
+#### 3.2.7 注意事项
+RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，需要先调用DisablePermute(C++)或`disable_permute(Python)，在预处理阶段禁用数据格式的转换。
+
+## 4. 其它文档
+- [ResNet50_vd Python 部署](../python)
+- [转换ResNet50_vd RKNN模型文档](../README.md)

docs/zh_CN/fastdeploy/rockchip/rknpu2/python/README.md

+# PaddleClas Python部署示例
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤
+- 1. 在部署前，需自行编译基于RKNPU2的Python预测库，参考文档[RKNPU2部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+- 2. 同时请用户参考[FastDeploy RKNPU2资源导航](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md)
+
+## 2. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以参考[RKNPU2模型转换](../README.md), 来准备模型.
+
+## 3. 部署示例
+
+本目录下提供`infer.py`快速完成 ResNet50_vd 在RKNPU上部署的示例
+
+```bash
+# 安装FastDpeloy RKNPU2 python包（详细文档请参考`部署环境准备`）
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/rockchip/rknpu2/python
+
+# 下载图片
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 推理
+python3 infer.py --model_file ./ResNet50_vd_infer/ResNet50_vd_infer_rk3588.rknn  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+
+# 运行完成后返回结果如下所示
+ClassifyResult(
+label_ids: 153,
+scores: 0.684570,
+)
+```
+
+## 4.注意事项
+RKNPU上对模型的输入要求是使用NHWC格式，且图片归一化操作会在转RKNN模型时，内嵌到模型中，因此我们在使用FastDeploy部署时，需要先调用DisablePermute(C++)或`disable_permute(Python)，在预处理阶段禁用数据格式的转换。
+
+## 5. 其它文档
+- [ResNet50_vd C++部署](../cpp)
+- [转换ResNet50_vd RKNN模型文档](../README.md)

docs/zh_CN/fastdeploy/rockchip/rv1126/README.md

+
+# PaddleClas 图像分类模瑞芯微NPU部署方案-FastDeploy
+
+## 1. 说明  
+本示例基于RV1126来介绍如何使用FastDeploy部署PaddleClas量化模型，支持如下芯片的部署：  
+- Rockchip RV1109
+- Rockchip RV1126
+- Rockchip RK1808
+
+## 2. 使用预导出的模型列表  
+
+FastDeploy提供预先量化好的模型进行部署. 更多模型, 欢迎用户参考[FastDeploy 一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/tree/develop/tools/common_tools/auto_compression) 来实现模型量化, 并完成部署.
+
+
+| 模型            | 量化方式 |
+|:---------------| :----- |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar)            | 离线量化 |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/mobilenetv1_ssld_ptq.tar)  | 离线量化 |
+
+## 3. 详细部署示例
+在 RV1126 上只支持 C++ 的部署。
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/rockchip/rv1126/cpp/README.md

+# PaddleClas rv1126 开发板 C++ 部署示例
+本目录下提供的 `infer.cc`，可以帮助用户快速完成 PaddleClas 量化模型在 rv1126 上的部署推理加速。
+
+## 1. 部署环境准备
+在部署前，需确认以下两个步骤
+- 1. 在部署前，需自行编译基于rv的预测库，参考文档[rv1126部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
+
+## 2. 量化模型准备
+1. 需要特别注意的是，在 rv1126 上部署的模型需要是量化后的模型. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
+2. 用户也可以使用 FastDeploy 提供的[一键模型自动化压缩工具](https://github.com/PaddlePaddle/FastDeploy/tree/develop/tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此 yaml 文件, 用户从 FP32 模型文件夹下复制此 yaml 文件到量化后的模型文件夹内即可.)
+
+## 3. 在 RV1126 上部署量化后的 ResNet50_Vd 分类模型
+请按照以下步骤完成在 RV1126 上部署 ResNet50_Vd 量化模型：
+1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
+
+2. 将编译后的库拷贝到当前目录，可使用如下命令：
+```bash
+cp -r FastDeploy/build/fastdeploy-timvx/ PaddleClas/deploy/fastdeploy/rockchip/rv1126/cpp/
+```
+
+3. 在当前路径下载部署所需的模型和示例图片：
+```bash
+cd PaddleClas/deploy/fastdeploy/rockchip/rv1126/cpp/
+mkdir models && mkdir images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
+tar -xvf resnet50_vd_ptq.tar
+cp -r resnet50_vd_ptq models
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+cp -r ILSVRC2012_val_00000010.jpeg images
+```
+
+4. 编译部署示例，可使入如下命令：
+```bash
+cd PaddleClas/deploy/fastdeploy/rockchip/rv1126/cpp/
+mkdir build && cd build
+cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=armhf ..
+make -j8
+make install
+# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
+```
+
+5. 基于 adb 工具部署 ResNet50 分类模型到 Rockchip RV1126，可使用如下命令：
+```bash
+# 进入 install 目录
+cd PaddleClas/deploy/fastdeploy/rockchip/rv1126/cpp/build/install/
+# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
+bash run_with_adb.sh infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg $DEVICE_ID
+```
+
+部署成功后运行结果如下：
+
+<img width="640" src="https://user-images.githubusercontent.com/30516196/200767389-26519e50-9e4f-4fe1-8d52-260718f73476.png">

docs/zh_CN/fastdeploy/serving/README.md

+# PaddleClas服务化部署示例
+
+PaddleClas 服务化部署示例是利用FastDeploy Serving搭建的服务化部署示例。FastDeploy Serving是基于Triton Inference Server框架封装的适用于高并发、高吞吐量请求的服务化部署框架，是一套可用于实际生产的完备且性能卓越的服务化部署框架.
+
+## 1. 部署环境准备
+在服务化部署前，需确认服务化镜像的软硬件环境要求和镜像拉取命令，请参考[FastDeploy服务化部署](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/README_CN.md)
+
+## 2. 启动服务
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/serving
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 将配置文件放入预处理目录
+mv ResNet50_vd_infer/inference_cls.yaml models/preprocess/1/inference_cls.yaml
+
+# 将模型放入 models/runtime/1目录下, 并重命名为model.pdmodel和model.pdiparams
+mv ResNet50_vd_infer/inference.pdmodel models/runtime/1/model.pdmodel
+mv ResNet50_vd_infer/inference.pdiparams models/runtime/1/model.pdiparams
+
+# 拉取fastdeploy镜像(x.y.z为镜像版本号，需参照serving文档替换为数字)
+# GPU镜像
+docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
+# CPU镜像
+docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10
+
+# 运行容器.容器名字为 fd_serving, 并挂载当前目录为容器的 /serving 目录
+nvidia-docker run -it --net=host --name fd_serving -v `pwd`/:/serving registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10  bash
+
+# 启动服务(不设置CUDA_VISIBLE_DEVICES环境变量，会拥有所有GPU卡的调度权限)
+CUDA_VISIBLE_DEVICES=0 fastdeployserver --model-repository=/serving/models --backend-config=python,shm-default-byte-size=10485760
+```
+>> **注意**:
+
+>> 拉取其他硬件上的镜像请看[服务化部署主文档](../../../../../serving/README_CN.md)
+
+>> 执行fastdeployserver启动服务出现"Address already in use", 请使用`--grpc-port`指定端口号来启动服务，同时更改客户端示例中的请求端口号.
+
+>> 其他启动参数可以使用 fastdeployserver --help 查看
+
+服务启动成功后， 会有以下输出:
+```
+......
+I0928 04:51:15.784517 206 grpc_server.cc:4117] Started GRPCInferenceService at 0.0.0.0:8001
+I0928 04:51:15.785177 206 http_server.cc:2815] Started HTTPService at 0.0.0.0:8000
+I0928 04:51:15.826578 206 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002
+```
+
+
+## 3. 客户端请求
+
+在物理机器中执行以下命令，发送grpc请求并输出结果
+```
+#下载测试图片
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+#安装客户端依赖
+python3 -m pip install tritonclient\[all\]
+
+# 发送请求
+python3 paddlecls_grpc_client.py
+```
+
+发送请求成功后，会返回json格式的检测结果并打印输出:
+```
+output_name: CLAS_RESULT
+{'label_ids': [153], 'scores': [0.6862289905548096]}
+```
+
+## 4. 配置修改
+
+当前默认配置在GPU上运行TensorRT引擎， 如果要在CPU或其他推理引擎上运行。 需要修改`models/runtime/config.pbtxt`中配置，详情请参考[配置文档](../../../../../serving/docs/zh_CN/model_configuration.md)
+
+## 5. 使用VisualDL进行可视化部署
+
+可以[使用 VisualDL 进行 Serving 可视化部署](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/vdl_management.md)，上述启动服务、配置修改以及客户端请求的操作都可以基于VisualDL进行。
+
+通过VisualDL的可视化界面对PaddleClas进行服务化部署只需要如下三步：
+```text
+1. 载入模型库：PaddleClas/deploy/fastdeploy/serving/models
+2. 下载模型资源文件：点击runtime模型，点击版本号1添加预训练模型，选择图像分类模型ResNet50_vd进行下载。
+3. 启动服务：点击启动服务按钮，输入启动参数。
+```
+ <p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211708702-828d8ad8-4e85-457f-9c62-12f53fc81853.gif" width="100%"/>
+</p>
+
+## 6. 常见问题
+- [如何编写客户端 HTTP/GRPC 请求](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/client.md)
+- [如何编译服务化部署镜像](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/compile.md)
+- [服务化部署原理及动态Batch介绍](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/demo.md)
+- [模型仓库介绍](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/model_repository.md)

docs/zh_CN/fastdeploy/sophgo/README.md

+# PaddleClas SOPHGO部署示例
+
+## 1. 说明  
+PaddleClas支持通过FastDeploy在SOPHGO上部署相关模型.
+
+## 2. 支持模型列表
+
+目前FastDeploy支持的如下模型的部署[ResNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
+
+## 3.准备ResNet部署模型以及转换模型
+
+SOPHGO-TPU部署模型前需要将Paddle模型转换成bmodel模型，具体步骤如下:
+- Paddle动态图模型转换为ONNX模型，请参考[Paddle2ONNX模型转换](https://github.com/PaddlePaddle/Paddle2ONNX/tree/develop/model_zoo/classification)
+- ONNX模型转换bmodel模型的过程，请参考[TPU-MLIR](https://github.com/sophgo/tpu-mlir)。
+
+下面以[ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz)为例子,教大家如何转换Paddle模型到SOPHGO-TPU模型。
+
+### 3.1 导出ONNX模型
+
+#### 3.1.1 下载Paddle ResNet50_vd静态图模型并解压
+```shell
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar xvf ResNet50_vd_infer.tgz
+```
+
+#### 3.1.2  静态图转ONNX模型，注意，这里的save_file请和压缩包名对齐
+```shell
+paddle2onnx --model_dir ResNet50_vd_infer \
+            --model_filename inference.pdmodel \
+            --params_filename inference.pdiparams \
+            --save_file ResNet50_vd_infer.onnx \
+            --enable_dev_version True
+```
+
+### 3.2 导出bmodel模型
+
+以转化BM1684x的bmodel模型为例子，我们需要下载[TPU-MLIR](https://github.com/sophgo/tpu-mlir)工程，安装过程具体参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
+
+#### 3.2.1	安装
+``` shell
+docker pull sophgo/tpuc_dev:latest
+
+# myname1234是一个示例，也可以设置其他名字
+docker run --privileged --name myname1234 -v $PWD:/workspace -it sophgo/tpuc_dev:latest
+
+source ./envsetup.sh
+./build.sh
+```
+
+#### 3.2.2	ONNX模型转换为bmodel模型
+``` shell
+mkdir ResNet50_vd_infer && cd ResNet50_vd_infer
+
+# 在该文件中放入测试图片，同时将上一步转换好的ResNet50_vd_infer.onnx放入该文件夹中
+cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
+cp -rf ${REGRESSION_PATH}/image .
+# 放入onnx模型文件ResNet50_vd_infer.onnx
+
+mkdir workspace && cd workspace
+
+# 将ONNX模型转换为mlir模型，其中参数--output_names可以通过NETRON查看
+model_transform.py \
+    --model_name ResNet50_vd_infer \
+    --model_def ../ResNet50_vd_infer.onnx \
+    --input_shapes [[1,3,224,224]] \
+    --mean 0.0,0.0,0.0 \
+    --scale 0.0039216,0.0039216,0.0039216 \
+    --keep_aspect_ratio \
+    --pixel_format rgb \
+    --output_names save_infer_model/scale_0.tmp_1 \
+    --test_input ../image/dog.jpg \
+    --test_result ResNet50_vd_infer_top_outputs.npz \
+    --mlir ResNet50_vd_infer.mlir
+
+# 将mlir模型转换为BM1684x的F32 bmodel模型
+model_deploy.py \
+  --mlir ResNet50_vd_infer.mlir \
+  --quantize F32 \
+  --chip bm1684x \
+  --test_input ResNet50_vd_infer_in_f32.npz \
+  --test_reference ResNet50_vd_infer_top_outputs.npz \
+  --model ResNet50_vd_infer_1684x_f32.bmodel
+```
+最终获得可以在BM1684x上能够运行的bmodel模型ResNet50_vd_infer_1684x_f32.bmodel。如果需要进一步对模型进行加速，可以将ONNX模型转换为INT8 bmodel，具体步骤参见[TPU-MLIR文档](https://github.com/sophgo/tpu-mlir/blob/master/README.md)。
+
+## 4. 其他链接
+- [Python部署](python)
+- [C++部署](cpp)

docs/zh_CN/fastdeploy/sophgo/cpp/README.md

+# PaddleClas C++部署示例
+
+本目录下提供`infer.cc`快速完成ResNet50_vd模型在SOPHGO BM1684x板子上加速部署的示例。
+
+## 1. 部署环境准备
+在部署前，需自行编译基于SOPHGO硬件的预测库，参考文档[SOPHGO硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
+
+## 2. 生成基本目录文件
+
+该例程由以下几个部分组成
+```text
+.
+├── CMakeLists.txt
+├── build  # 编译文件夹
+├── image  # 存放图片的文件夹
+├── infer.cc
+├── preprocess_config.yaml #示例前处理配置文件
+└── model  # 存放模型文件的文件夹
+```
+## 3. 部署实例
+
+
+### 3.1 编译并拷贝SDK到thirdpartys文件夹
+
+请参考[SOPHGO部署库编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/sophgo.md)仓库编译SDK，编译完成后，将在build目录下生成fastdeploy-x.x.x目录.
+
+### 3.2 拷贝模型文件，以及配置文件至model文件夹
+将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README.md)  
+将转换后的SOPHGO bmodel模型文件拷贝至model中  
+将前处理配置文件也拷贝到model中  
+```bash
+cp preprocess_config.yaml ./model
+```
+
+### 3.3 准备测试图片至image文件夹
+```bash
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+cp ILSVRC2012_val_00000010.jpeg ./images
+```
+
+### 3.4 编译example
+
+```bash
+cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-x.x.x
+make
+```
+
+### 3.5 运行例程
+
+```bash
+./infer_demo model images/ILSVRC2012_val_00000010.jpeg
+```
+
+## 4. 其它文档
+- [ResNet50_vd python部署](../python)
+- [转换ResNet50_vd SOPHGO模型文档](../README.md)

docs/zh_CN/fastdeploy/sophgo/python/README.md

+# PaddleClas Python部署示例
+本目录下提供`infer.py`快速完成 ResNet50_vd 在SOPHGO TPU上部署的示例.
+
+
+## 1. 部署环境准备
+
+在部署前，需自行编译基于算能硬件的FastDeploy python wheel包并安装，参考文档[算能硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
+
+## 2.运行部署示例
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas/deploy/fastdeploy/sophgo/python
+
+# 下载图片
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 推理转换好的模型
+python3 infer.py --model_file ./bmodel/resnet50_1684x_f32.bmodel  --config_file ResNet50_vd_infer/inference_cls.yaml  --image ILSVRC2012_val_00000010.jpeg
+
+# 运行完成后返回结果如下所示
+ClassifyResult(
+label_ids: 153,
+scores: 0.684570,
+)
+```
+
+## 4. 其它文档
+- [ResNet50_vd C++部署](../python)
+- [转换ResNet50_vd SOPHGO模型文档](../README.md)

docs/zh_CN/fastdeploy/web/README.md

+# MobileNet 前端部署示例
+
+本节介绍部署PaddleClas的图像分类mobilenet模型在浏览器中运行，以及@paddle-js-models/mobilenet npm包中的js接口。
+
+## 1. 前端部署图像分类模型
+
+图像分类模型web demo使用[**参考文档**](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/application/js/web_demo)
+
+## 2. MobileNet js接口
+
+```
+import * as mobilenet from "@paddle-js-models/mobilenet";
+# mobilenet模型加载和初始化
+await mobilenet.load()
+# mobilenet模型执行预测，并获得分类的类别
+const res = await mobilenet.classify(img);
+console.log(res);
+```
+
+**load()函数参数**
+
+> * **Config**(dict): 图像分类模型配置参数，默认值为 {Path: 'https://paddlejs.bj.bcebos.com/models/fuse/mobilenet/mobileNetV2_fuse_activation/model.json', fill: '#fff', mean: [0.485, 0.456, 0.406],std: [0.229, 0.224, 0.225]}; 其中，modelPath为js模型路径，fill 为图像预处理padding的值，mean和std分别为预处理的均值和标准差。
+
+
+**classify()函数参数**
+> * **img**(HTMLImageElement): 输入图像参数，类型为HTMLImageElement。