Merge branch 'newest' into fix_deploy_python_doc

deploy/configs/inference_cls_multilabel.yaml

@@ -27,9 +27,10 @@ PreProcess:
     - ToCHWImage:
 
 PostProcess:
-  main_indicator: MultiLabelTopk
-  MultiLabelTopk:
-    topk: 5
-    class_id_map_file: None
+  main_indicator: MultiLabelThreshOutput
+  MultiLabelThreshOutput:
+    threshold: 0.5
+    class_id_map_file: "../ppcls/utils/NUS-WIDE-SCENE_label_list.txt"
+    delimiter: " "
   SavePreLabel:
     save_dir: ./pre_label/

deploy/python/postprocess.py

@@ -82,10 +82,11 @@ class ThreshOutput(object):
 
 
 class Topk(object):
-    def __init__(self, topk=1, class_id_map_file=None):
+    def __init__(self, topk=1, class_id_map_file=None, delimiter=None):
         assert isinstance(topk, (int, ))
-        self.class_id_map = self.parse_class_id_map(class_id_map_file)
         self.topk = topk
+        self.class_id_map = self.parse_class_id_map(class_id_map_file)
+        self.delimiter = delimiter if delimiter is not None else " "
 
     def parse_class_id_map(self, class_id_map_file):
         if class_id_map_file is None:
@@ -102,21 +103,20 @@ class Topk(object):
             with open(class_id_map_file, "r") as fin:
                 lines = fin.readlines()
                 for line in lines:
-                    partition = line.split("\n")[0].partition(" ")
+                    partition = line.split("\n")[0].partition(self.delimiter)
                     class_id_map[int(partition[0])] = str(partition[-1])
         except Exception as ex:
             print(ex)
             class_id_map = None
         return class_id_map
 
-    def __call__(self, x, file_names=None, multilabel=False):
+    def __call__(self, x, file_names=None):
         if file_names is not None:
             assert x.shape[0] == len(file_names)
         y = []
         for idx, probs in enumerate(x):
             index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
-                "int32") if not multilabel else np.where(
-                    probs >= 0.5)[0].astype("int32")
+                "int32")
             clas_id_list = []
             score_list = []
             label_name_list = []
@@ -138,12 +138,56 @@ class Topk(object):
         return y
 
 
-class MultiLabelTopk(Topk):
-    def __init__(self, topk=1, class_id_map_file=None):
-        super().__init__()
+class MultiLabelThreshOutput(object):
+    def __init__(self, threshold=0.5, class_id_map_file=None, delimiter=None):
+        self.threshold = threshold
+        self.delimiter = delimiter if delimiter is not None else " "
+        self.class_id_map = self.parse_class_id_map(class_id_map_file)
+
+    def parse_class_id_map(self, class_id_map_file):
+        if class_id_map_file is None:
+            return None
+
+        if not os.path.exists(class_id_map_file):
+            print(
+                "Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
+            )
+            return None
+
+        try:
+            class_id_map = {}
+            with open(class_id_map_file, "r") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    partition = line.split("\n")[0].partition(self.delimiter)
+                    class_id_map[int(partition[0])] = str(partition[-1])
+        except Exception as ex:
+            print(ex)
+            class_id_map = None
+        return class_id_map
 
     def __call__(self, x, file_names=None):
-        return super().__call__(x, file_names, multilabel=True)
+        y = []
+        for idx, probs in enumerate(x):
+            index = np.where(probs >= self.threshold)[0].astype("int32")
+            clas_id_list = []
+            score_list = []
+            label_name_list = []
+            for i in index:
+                clas_id_list.append(i.item())
+                score_list.append(probs[i].item())
+                if self.class_id_map is not None:
+                    label_name_list.append(self.class_id_map[i.item()])
+            result = {
+                "class_ids": clas_id_list,
+                "scores": np.around(
+                    score_list, decimals=5).tolist(),
+                "label_names": label_name_list
+            }
+            if file_names is not None:
+                result["file_name"] = file_names[idx]
+            y.append(result)
+        return y
 
 
 class SavePreLabel(object):

docs/en/quick_start/quick_start_multilabel_classification_en.md

@@ -51,6 +51,9 @@ python3 -m paddle.distributed.launch \
 
 After training 10 epochs, the best correctness of the validation set should be around 0.95.
 
+**Note:**
+1. Currently, the loss function for multi-label classification only supports `MultiLabelLoss` (BCE Loss).
+2. Currently, the evaluation metrics of multi-label classification support `Accuracy_Score` and `HammingDistance`. Please look forward to the support of other evaluation metrics.
 
 <a name="3"></a>
 ## 3. Model Evaluation
@@ -74,7 +77,7 @@ python3 tools/infer.py \
 Obtain an output silimar to the following:
 
 ```
-[{'class_ids': [6, 13, 17, 23, 26, 30], 'scores': [0.95683, 0.5567, 0.55211, 0.99088, 0.5943, 0.78767], 'file_name': './deploy/images/0517_2715693311.jpg', 'label_names': []}]
+[{'class_ids': [6, 13, 17, 23, 30], 'scores': [0.98217, 0.78129, 0.64377, 0.9942, 0.96109], 'label_names': ['clouds', 'lake', 'ocean', 'sky', 'water'], 'file_name': 'deploy/images/0517_2715693311.jpg'}]
 ```
 
 
@@ -107,11 +110,14 @@ Inference and prediction through predictive engines:
 
 ```
 python3 python/predict_cls.py \
-     -c configs/inference_multilabel_cls.yaml
+     -c configs/inference_cls_multilabel.yaml
 ```
+The predicted pictures are as follows:
 
-Obtain an output silimar to the following:
+![](../../images/quick_start/multi_label_demo.png)
+
+After executing the inference command, obtain an output silimar to the following:
 
 ```
-0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): []
+0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 30], score(s): [0.98, 0.78, 0.64, 0.99, 0.96], label_name(s): ['clouds', 'lake', 'ocean', 'sky', 'water']
 ```

docs/zh_CN/Makefile

-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-
-#SOURCEDIR     = source
-BUILDDIR      = build
-
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/zh_CN/advanced_tutorials/gallery2fc.md

-# 识别模型转分类模型
-
-PaddleClas 提供了 `gallery2fc.py` 工具，帮助大家将识别模型转为分类模型。目前该工具仅支持转换量化后模型，因此建议使用 PaddleClas 提供的 `general_PPLCNet_x2_5_pretrained_v1.0_quant` 预训练模型，该模型为量化后的通用识别模型，backbone 为 PPLCNet_x2_5。
-
-如需使用其他模型，关于量化的具体操作请参考文档 [模型量化](./model_prune_quantization.md)。
-
-## 一、模型转换说明
-
-### 1.1 准备底库数据、预训练模型
-
-#### 1. 底库数据集
-
-首先需要准备好底库数据，下面以 PaddleClas 提供的饮料数据集（drink_dataset_v1.0）为例进行说明，饮料数据集获取方法：
-
-```shell
-cd PaddleClas/
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
-tar -xf drink_dataset_v1.0.tar
-```
-
-饮料数据集的底库图片路径为 `drink_dataset_v1.0/gallery/`，底库图片列表可在 `drink_dataset_v1.0/gallery/drink_label.txt` 中查看，关于底库数据格式说明，请参考文档[数据集格式说明](../data_preparation/recognition_dataset.md#1-数据集格式说明)。
-
-#### 2. 预训练模型
-
-在开始转换模型前，需要准备好预训练模型，下面以量化后的 `general_PPLCNet_x2_5` 模型为例，下载预训练模型：
-
-```shell
-cd PaddleClas/pretrained/
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0_quant.pdparams
-```
-
-### 1.2 准备配置文件
-
-在进行模型转换时，需要通过配置文件定义所需参数，本例中所用配置文件为 `ppcls/configs/GeneralRecognition/Gallery2FC_PPLCNet_x2_5.yaml`，对于配置文件字段的说明，如下所示：
-
-* Global:
-    * pretrained_model: 预训练模型路径，无需包含 `.pdparams` 后缀名；
-    * image_shape: 模型输入数据尺寸，无需包含 batch size 维度；
-    * save_inference_dir: 转换后模型的保存路径；
-* Arch: 模型结构相关定义，可参考 [配置说明](../models_training/config_description.md#3-%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)；
-* IndexProcess: 底库数据集相关定义
-    * image_root: 底库数据集路径；
-    * data_file: 底库数据集列表文件路径；
-
-### 1.3 模型转换
-
-在完成上述准备工作后，即可进行模型转换，命令如下所示：
-
-```python
-python ppcls/utils/gallery2fc.py -c ppcls/configs/GeneralRecognition/Gallery2FC_PPLCNet_x2_5.yaml
-```
-
-在上述命令执行完成后，转换并导出的模型保存在目录 `./inference/general_PPLCNet_x2_5_quant/` 下。在推理部署时，需要注意的是，模型的输出结果通常有多个，应选取分类结果作为模型输出，需要注意区分。

docs/zh_CN/advanced_tutorials/index.rst

-高级教程
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   DataAugmentation.md
-   knowledge_distillation.md
-   model_prune_quantization.md
-   code_overview.md
-   how_to_contribute.md
-   
-   

docs/zh_CN/algorithm_introduction/index.rst

-算法介绍
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   image_classification.md
-   metric_learning.md
-   knowledge_distillation.md
-   model_prune_quantization.md
-   ImageNet_models.md
-   DataAugmentation.md

docs/zh_CN/others/course_link.md → docs/zh_CN/community/courses.md

@@ -29,6 +29,3 @@
     - 产业应用十问十答
     - 智能零售下的应用案例
     - 识别系统快速落地方案
-  
-  
-

docs/zh_CN/conf.py

-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-import sphinx_rtd_theme
-from recommonmark.parser import CommonMarkParser
-#import sphinx-markdown-tables
-# -- Project information -----------------------------------------------------
-
-project = 'PaddleClas'
-copyright = '2021, PaddleClas'
-author = 'PaddleClas'
-
-# The full version, including alpha/beta/rc tags
-release = '2.3.0'
-
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-source_parsers = {
-    '.md': CommonMarkParser,
-}
-source_suffix = ['.rst', '.md']
-extensions = [
-     'recommonmark',
-     'sphinx_markdown_tables'
- ]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = 'zh_CN'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "sphinx_rtd_theme"
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']

docs/zh_CN/data_preparation/index.rst

-数据准备
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   recognition_dataset.md
-   classification_dataset.md

docs/zh_CN/inference_deployment/shitu_gallery_manager.md → docs/zh_CN/deployment/PP-ShiTu/gallery_manager.md

@@ -192,4 +192,3 @@ index_root/            # 库存储目录
 - 问题4: 报错 图像与index库不一致
 
   答：可能用户自己修改了image_list.txt，修改完成后，请及时更新index库，保证其一致。
-

docs/zh_CN/faq_series/index.rst

-FAQ系列
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   faq_2021_s2.md
-   faq_2021_s1.md
-   faq_2020_s1.md
-   faq_selected_30.md

docs/zh_CN/image_recognition_pipeline/index.rst

-图像识别流程
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   mainbody_detection.md
-   feature_extraction.md
-   vector_search.md

docs/zh_CN/index.rst

-欢迎使用PaddleClas图像分类库！
-================================
-
-.. toctree::
-   :maxdepth: 1
-
-   introduction/index
-   installation/index
-   quick_start/index
-   image_recognition_pipeline/index
-   data_preparation/index
-   models_training/index
-   inference_deployment/index
-   models/index
-   algorithm_introduction/index
-   advanced_tutorials/index
-   others/index
-   faq_series/index

docs/zh_CN/inference_deployment/index.rst

-推理部署
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   export_model.md
-   python_deploy.md
-   cpp_deploy.md
-   paddle_serving_deploy.md
-   paddle_hub_serving_deploy.md
-   paddle_lite_deploy.md
-   whl_deploy.md
-   cpp_deploy_on_windows.md
-   
-   
-   

docs/zh_CN/installation/index.rst

-安装
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   install_paddle.md
-   install_paddleclas.md

docs/zh_CN/introduction/function_intro.md

-## PaddleClas 功能特性
-
-飞桨图像识别套件 PaddleClas 是飞桨为工业界和学术界所准备的一个图像识别任务的工具集，助力使用者训练出更好的视觉模型和应用落地。具体地，它包含以下几个核心特性。
-
-
-- 实用的图像识别系统：集成了目标检测、特征学习、图像检索等模块，广泛适用于各类图像识别任务。
-提供商品识别、车辆识别、 logo 识别和动漫人物识别等 4 个场景应用示例。
-
-- 丰富的预训练模型库：提供了 36 个系列共 175 个 ImageNet 预训练模型，其中 7 个精选系列模型支持结构快速修改。
-
-- 全面易用的特征学习组件：集成 arcmargin, triplet loss 等 12 度量学习方法，通过配置文件即可随意组合切换。
-
-- SSLD 知识蒸馏： 14 个分类预训练模型，精度普遍提升 3% 以上；其中 ResNet50_vd 模型在 ImageNet-1k 数据集上的 Top-1 精度达到了 84.0%，Res2Net200_vd 预训练模型 Top-1 精度高达 85.1% 。
-
-- 数据增广：支持 AutoAugment 、 Cutout 、 Cutmix 等 8 种数据增广算法详细介绍、代码复现和在统一实验环境下的效果评估。
-
-
-![](../../images/recognition.gif)
-
-更多关于图像识别快速体验、算法详解、模型训练评估与预测部署方法，请参考[首页 README 文档教程](../../../README_ch.md)。

docs/zh_CN/introduction/index.rst

-介绍
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   function_intro.md
-   more_demo/index

docs/zh_CN/introduction/more_demo/cartoon.md

-## 动漫人物图片识别效果图
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069080-a821e0b7-8a10-4946-bf05-ff093cc16064.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069100-7539d292-1bd8-4655-8a6d-d1f2238bd618.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069103-f91359d4-1197-4a6e-b2f7-434c76a6b704.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069108-ad54ae1d-610d-4cfa-9cd6-8ee8d280d61d.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069114-3c771434-84a8-4e58-961e-d35edfbfe5ef.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069119-e8d85be5-da87-4125-ae8b-9fd4cac139d9.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069124-98c30894-4837-4f2f-8399-3d3ebadfd0a1.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069125-a9edf115-33a1-48bf-9e4f-7edbc4269a1e.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069129-98553a25-00e2-4f0f-9b44-dfc4e4f6b6d1.png " width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069131-f7649bb2-255c-4725-a635-799b8b4d815a.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069135-acb69b89-55db-41ac-9846-e2536ef3d955.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069137-1f0abfdb-6608-432e-bd40-c8e1ab86ef8b.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069140-18c6a439-f117-498d-9cdb-ade71cc2c248.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069145-80452f86-afcf-42b5-8423-328cca9e4750.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069154-63a25c1c-b448-44c2-8baf-eb31952c5476.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069156-1b881c6b-5680-4f9a-aef1-2491af50675d.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069161-8759f3d4-8456-43ea-bf54-99a646d5a109.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069167-937aa847-c661-431c-b3dc-5a3c890b31cd.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069170-43d0dce4-6c62-485d-adf4-364c8467c251.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069175-70bc9e50-b833-4a2a-8a3f-c0775dac49c2.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069179-d01f8a0f-4383-4b08-b064-4e6bb006e745.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069184-d423a84c-c9dd-4125-9dc7-397cae21efc9.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069188-fc4deb80-38a2-4c50-9a29-30cee4c8e374.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069193-77a19ee8-b1e2-4c27-9016-3440a1547470.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069196-5f050524-ac08-4831-89f5-9e9e3ce085c1.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069200-4f963171-c790-4f43-8ca3-2e701ad3731c.jpeg" width = "400" /> </div>

docs/zh_CN/introduction/more_demo/index.rst

-介绍
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   cartoon.md
-   logo.md
-   more_demo.md
-   product.md
-   vehicle.md

docs/zh_CN/introduction/more_demo/logo.md

-## 商标图片识别效果图
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096687-5b562e2d-0653-4be6-861d-1936a4440df2.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096698-4b95eb4b-6638-47dc-ae48-7b40744a31ba.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096701-4a4b2bd9-85f2-4d55-be4b-be6ab5e0fb81.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096706-ef4ad024-7284-4cb3-975a-779fd06b96f5.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096710-620b0495-cc83-4501-a104-dfe20afb53d2.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096713-48e629aa-c637-4603-b005-18570fa94d6d.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096715-709957f2-50bb-4edb-a6e4-e7d5601872c7.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096717-a74744cc-4fb8-4e78-b1cb-20409582ca52.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096721-d4af003c-7945-4591-9e47-4e428dc2628c.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096726-460af6ab-8595-4fb4-9960-4c66b18bee1e.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096728-81494000-92b5-40ad-a6a7-606dae3548a3.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096731-2e980977-9ee6-4e29-bdf7-8397820f70e8.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096732-7d425b45-6b04-4984-948d-278da13dd802.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096735-a9c85c14-5965-4529-a235-ce00035bd7ab.jpg " width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096736-3182efc6-ba43-4cde-9397-88a131f4fed8.jpg " width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096737-91e6fa24-1eb5-4aba-9271-5a3722cbe35b.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096740-f440f89b-5f95-493a-b087-00c7cd3481ef.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096747-31b924e3-ffb2-45ab-872e-4ff923ed04f1.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096752-1f98c937-5d83-4c29-b495-01971b5fb258.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096756-a994c7e2-b9e7-40ba-9934-78c10666217b.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096757-879749e0-9e04-4d1e-a07b-6a4322975a84.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096761-5b682ce8-4f83-4fbb-bfb7-df749912aa8b.png " width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096767-e8f701eb-d0e8-4304-b031-e2bff8c199f3.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096778-ec2ad374-b9fc-427e-9e8b-8e5d2afc6394.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096783-9ec5e04d-19e3-463d-ad9d-7a26202bbb9c.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096788-44f04979-18ca-4ba6-b833-7489b344ffff.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096791-6989451e-157c-4101-8b54-7578b05eb7c9.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096796-cc4477cf-016c-4b19-86c3-61824704ecf5.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096798-ba33ee0d-45b8-48ad-a8fa-14cd643a6976.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096805-e29a2ba8-4785-4ca6-9e0d-596fad6ce8dc.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096812-7d8c57a5-fbae-4496-8144-3b40ac74fef0.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096816-50f0ac3d-f2eb-4011-a34e-58e2e215b7b0.jpg " width = "400" /> </div>

docs/zh_CN/introduction/more_demo/more_demo.md

-## 识别效果展示
-- 商品识别
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277277-7b29f596-35f6-4f00-8d2b-0ef0be57a090.jpg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277291-f7d2b2a1-5790-4f5b-a0e6-f5c52d04a69a.jpg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277300-8ce0d5ce-e0ca-46ea-bb9a-74df0df66ae3.jpg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277308-14a097bd-2bcd-41ce-a9e6-5e9cd0bd8b08.jpg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277311-208ae574-a708-46e2-a41e-c639322913b1.jpg" width = "400" /> </div>
-
-[更多效果图](product.md)
-
-
-- 动漫人物识别
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069108-ad54ae1d-610d-4cfa-9cd6-8ee8d280d61d.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069100-7539d292-1bd8-4655-8a6d-d1f2238bd618.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140069080-a821e0b7-8a10-4946-bf05-ff093cc16064.jpeg" width = "400" /> </div>
-
-[更多效果图](cartoon.md)
-
-
-- logo识别
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096687-5b562e2d-0653-4be6-861d-1936a4440df2.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096701-4a4b2bd9-85f2-4d55-be4b-be6ab5e0fb81.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096706-ef4ad024-7284-4cb3-975a-779fd06b96f5.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096713-48e629aa-c637-4603-b005-18570fa94d6d.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096752-1f98c937-5d83-4c29-b495-01971b5fb258.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140096767-e8f701eb-d0e8-4304-b031-e2bff8c199f3.jpeg" width = "400" /> </div>
-
-
-[更多效果图](logo.md)
-
-
-- 车辆识别
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243899-c60f0a51-db9b-438a-9f2d-0d2893c200bb.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243905-7eeb938d-d88f-4540-a667-06e08dcf1f55.jpeg" width = "400" /> </div>
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243911-735a6ec0-a624-4965-b3cd-2b9f52fa8d65.jpeg" width = "400" /> </div>
-
-[更多效果图](vehicle.md)

docs/zh_CN/introduction/more_demo/product.md

-## 商品图片识别效果图
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277277-7b29f596-35f6-4f00-8d2b-0ef0be57a090.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277287-7bdad02a-8e3c-4e04-861c-95a5dae1f3c6.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277291-f7d2b2a1-5790-4f5b-a0e6-f5c52d04a69a.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277294-80aaab94-5109-41be-97f8-3ada73118963.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277296-2a8d7846-cd2e-454e-8b72-46233da09451.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277300-8ce0d5ce-e0ca-46ea-bb9a-74df0df66ae3.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277302-25c973eb-f9aa-42ce-b9e9-66cee738c241.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277303-3d3460da-c6aa-4994-b585-17bc9f3df504.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277306-20cbef71-cc58-4ae1-965b-4806e82988a9.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277308-14a097bd-2bcd-41ce-a9e6-5e9cd0bd8b08.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277309-be092d1c-6513-472c-8b7f-685f4353ae5b.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277311-208ae574-a708-46e2-a41e-c639322913b1.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277314-72901737-5ef5-4a23-820b-1db58c5e6ca0.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277318-aef4080c-24f2-4d92-be3c-45b500b75584.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277320-8046d0df-1256-41ce-a8d6-6d2c1292462c.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277321-e3864473-6a8e-485f-81f2-562b902d6cff.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277324-0aacc27f-699a-437b-bac0-4a20c90b47b1.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277328-8d28f754-8645-4c05-a9a6-0312bbe2f890.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277333-59da1513-e7e5-455c-ab73-7a3162216923.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277335-454c0423-5398-4348-aaab-e2652fd08999.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277338-a7d09c28-1b86-4cf5-bd79-99d51c5b5311.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277343-9c456d21-8018-4cd5-9c0b-cc7c087fac69.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277345-2ef780f1-d7c9-4cf2-a370-f220a052eb71.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277347-baa4b870-7fca-4d4c-8528-fad720270024.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277351-e0691080-ede4-49ae-9075-d36a41cebf25.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277354-509f8f85-f171-44e9-8ca1-4c3cae77b5fb.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277357-39d572b8-60ee-44db-9e0e-2c0ea2be2ed3.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277359-6caf33f6-2a38-48e5-b349-f4dd1ef2566b.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277362-260daa87-1db7-4f89-ba9c-1b32876fd3b6.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277368-b0e96341-e030-4e4d-8010-6f7c3bc94d2f.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277370-1f26e4e5-9988-4427-a035-44bfd9d472d6.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277372-27e60b60-cd5c-4b05-ae38-2e9524c627f3.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277374-bd292bb2-e1f9-4d5f-aa49-d67ac571d01b.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277377-b0b8cdb9-8196-4598-ae47-b615914bf6bf.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277382-fc89d18a-a57b-4331-adbb-bda3584fb122.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277390-83f66d3f-c415-47e6-b651-6b51fbe59bbf.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277396-9e5e3aa3-6d9e-40ab-a325-2edea452156d.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277399-b92e2092-eabd-45c8-bf36-b2e238167892.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277404-285f943a-de70-48b8-9545-53e229b7350d.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277408-4f9b8b19-42c2-4ba4-bf6d-b95ababe0313.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277409-6df0faf7-71b7-4c9a-a875-36ae7ee7129d.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277411-9c2b364a-749d-465e-a85d-29a69e9ff3ef.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277413-c54a462c-dd3b-4ad0-985d-ef0ec1f216ec.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277429-521ac9a6-e4c3-4c74-9c5b-8e8dd6cddf34.jpg" width = "400" /> </div>
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-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277433-4f9fb9c8-7930-4303-b54e-a6eace347923.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277434-f3aa3565-a2c5-4c1c-ab44-930a8b073b5f.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277437-90cf1cd7-6a62-4ac4-ac85-3aa534e50cee.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277439-54e168bc-9518-429e-9e97-cb9ca5e811c9.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277441-a3c277d7-c889-4556-b74a-400cadf8b771.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277442-22a0cd38-acd8-4b5a-8e59-c4bea852fb79.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277444-ea662034-c17f-47ba-9ea3-694d3cb0c880.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277448-a71f4a0a-c3cc-4432-a803-843b7c65307f.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277449-0b3a2e98-3e09-4bd6-be32-c35f44154e8a.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277452-e36ccc63-8e39-4973-a336-4ace855d25e6.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277454-bddd9527-b189-4771-ab9e-52085db5a44d.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277455-7ea277ba-bc75-48db-9567-40e1acb56f02.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277460-0f5ee4dc-5ece-45d5-8ef9-666f1be41b76.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277461-37cab773-6341-4c91-b1f4-780d530eab3b.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277465-8f53ef9d-0465-4a90-afac-b1dd3c970b72.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277467-655ddabe-cbe0-4d1f-a30e-c2965428e8d7.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277470-4587e905-3fc8-4dad-84ee-0844ba4d2474.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277473-a155944f-efe3-492a-babc-2f3fe700a99b.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277475-c95ab821-f5ae-427a-8721-8991f9c7f29f.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277479-55b59855-2ed6-4526-9481-6b92b25fef97.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277484-556f0e4c-007b-4f6a-b21f-c485f630cbcb.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277486-a39eb069-bc13-415e-b936-ba294216dfac.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277487-80952841-6a76-4fb3-8049-fe15ce8f7cfb.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277491-e892a6a8-6f9a-46c7-83e0-261cfb92d276.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277494-520f483e-654d-4399-9684-1fcd9778b76e.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277496-54b1ada5-e6a6-4654-a8a6-739511cec750.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277500-ff7e2afd-9cd7-484a-bd1e-362226f5197f.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277501-94489261-bea5-4492-bf3e-98cc8aaa7a7f.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277504-567a32bc-a573-4154-a9cd-6acbec923768.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277506-e893d4d5-43ce-4df1-9f08-3cdf6a8c7e2c.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277509-5766629f-bb92-4552-b34a-647e29b9a89b.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277511-8821708b-09f0-4aab-86dd-40ae3794697a.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277515-ed6a0dff-bd91-4233-a9af-e2744df7c7e0.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277519-1883d6a1-9348-4514-8924-dde27dd38704.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277524-b9d8515c-4df2-410a-b4a6-da098cb9da61.jpg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140277526-52a9c666-a799-4921-b371-41d97d7d9242.jpg" width = "400" /> </div>

docs/zh_CN/introduction/more_demo/vehicle.md

-## 车辆图片识别效果图
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243899-c60f0a51-db9b-438a-9f2d-0d2893c200bb.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243904-fdbe2e01-dc7c-449a-8e9e-baea4f85fee4.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243905-7eeb938d-d88f-4540-a667-06e08dcf1f55.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243908-c7f1e3ea-92a7-429b-888c-732b9ec5398f.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243911-735a6ec0-a624-4965-b3cd-2b9f52fa8d65.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243913-baec489a-5463-472b-b5d1-418bcd4eb978.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243916-f50dfcdd-2d5f-48f9-876f-dbc05f4afa30.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243920-7a65ec82-8312-421e-985a-c394f11af28f.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243922-458e6dca-fb80-4baf-951e-9651080dc242.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243926-5df3036b-9ea1-441c-b30a-b4f847df25ab.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243927-7673d94a-fbb0-4a92-a3f3-c879a432a7db.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243928-91082855-c5a7-4a3f-aeea-7a2e51e43183.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243929-88fe7efa-b212-4105-af2f-2248a6cb2877.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243933-49e71d02-8228-40ec-99b2-3ed862bf4ba5.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243935-530fbfa3-0d34-4d9d-bd59-2fde5659f7e5.jpeg" width = "400" /> </div>
-
-<div align="center"> <img src="https://user-images.githubusercontent.com/12560511/140243940-d289fc7d-d343-4aa5-a807-9ce09a241ccd.jpeg" width = "400" /> </div>

docs/zh_CN/make.bat

-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-
-set SOURCEDIR=source
-set BUILDDIR=build
-
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd

docs/zh_CN/models/HarDNet.md → docs/zh_CN/models/ImageNet1k/HarDNet.md

@@ -32,4 +32,3 @@ HarDNet（Harmonic DenseNet）是 2019 年由国立清华大学提出的一种
 | HarDNet85    | 224       | 256               | 6.24                           | 14.85                          | 20.57                          |
 | HarDNet39_ds | 224       | 256               | 1.40                           | 2.30                           | 3.33                           |
 | HarDNet68_ds | 224       | 256               | 2.26                           | 3.34                           | 5.06                           |
-

docs/zh_CN/models/PP-HGNet.md → docs/zh_CN/models/ImageNet1k/PP-HGNet.md

@@ -404,4 +404,3 @@ PaddleClas 提供了基于 Paddle Lite 来完成模型端侧部署的示例，
 Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式。通过 ONNX 可以完成将 Paddle 模型到多种推理引擎的部署，包括TensorRT/OpenVINO/MNN/TNN/NCNN，以及其它对 ONNX 开源格式进行支持的推理引擎或硬件。更多关于 Paddle2ONNX 的介绍，可以参考[Paddle2ONNX 代码仓库](https://github.com/PaddlePaddle/Paddle2ONNX)。
 
 PaddleClas 提供了基于 Paddle2ONNX 来完成 inference 模型转换 ONNX 模型并作推理预测的示例，您可以参考[Paddle2ONNX 模型转换与预测](../../../deploy/paddle2onnx/readme.md)来完成相应的部署工作。
-

docs/zh_CN/models/ImageNet1k/README.md

+model_list.md
\ No newline at end of file

docs/zh_CN/models/PULC/README.md

+model_list.md
\ No newline at end of file

docs/zh_CN/models/index.rst

-模型库
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   DPN_DenseNet.md
-   models_intro.md
-   RepVGG.md
-   EfficientNet_and_ResNeXt101_wsl.md
-   ViT_and_DeiT.md
-   SwinTransformer.md
-   Others.md
-   SEResNext_and_Res2Net.md
-   ESNet.md
-   HRNet.md
-   ReXNet.md
-   Inception.md
-   TNT.md
-   RedNet.md
-   DLA.md
-   ResNeSt_RegNet.md
-   PP-LCNet.md
-   HarDNet.md
-   ResNet_and_vd.md
-   LeViT.md
-   Mobile.md
-   MixNet.md
-   Twins.md

docs/zh_CN/models_training/index.rst

-模型训练
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   config_description.md
-   classification.md
-   recognition.md
-   train_strategy.md

docs/zh_CN/others/index.rst

-其他
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   transfer_learning.md
-   train_with_DALI.md
-   VisualDL.md
-   train_on_xpu.md
-   feature_visiualization.md
-   paddle_mobile_inference.md
-   course_link.md
-   competition_support.md
-   update_history.md
-   versions.md
-   
-   
-   
-   

docs/zh_CN/others/versions.md

-# 版本更新信息
-----------
-## 目录
-* [1. v2.3](#1)
-* [2. v2.2](#2)
-
-<a name='1'></a>
-
-## 1. v2.3
-
-- 模型更新
-  - 添加轻量化模型预训练权重，包括检测模型、特征模型
-  - 发布 PP-LCNet 系列模型，此系列模型是专门在 CPU 上设计运行的自研模型
-  - SwinTransformer、Twins、Deit 支持从 scrach 直接训练，达到论文精度
-- 框架基础能力
-  - 添加 DeepHash 模块，支持特征模型直接输出二值特征
-  - 添加 PKSampler，特征模型不能多机多卡的训练的问题
-  - 支持 PaddleSlim：支持分类模型、特征模型的量化、裁剪训练及离线量化功能
-  - Legendary models 支持模型中间结果输出
-  - 支持多标签分类训练
-- 预测部署
-  - 使用 Faiss 替换原有特征检索库，提升平台适配性
-  - 支持 PaddleServing：支持分类模型、图像识别流程的部署
-
-- 推荐库版本
-  - python 版本：3.7
-  - PaddlePaddle 版本：2.1.3
-  - PaddleSlim 版本：2.2.0
-  - PaddleServing 版本：0.6.1
-
-<a name='2'></a>
-
-## 2. v2.2
-
-- 模型更新
-  - 添加 LeViT、Twins、TNT、DLA、HardNet、RedNet、SwinTransfomer 模型
-- 框架基础能力
-  - 将分类模型分为两类
-    -  legendary models：引入 TheseusLayer 基类，及增加了修改网络功能接口，同时支持网络截断输出功能
-    - model zoo：其他普通分类模型
-  - 添加 Metric Learning 算法支持
-    - 添加多种相关 Loss 算法，及基础网络模块 gears（支持与 backbone、loss 组合）方便使用
-    - 同时支持普通分类及 metric learning 相关任务训练
-  - 支持静态图训练
-  - 分类训练支持 dali 加速
-  - 支持 fp16 训练
-- 应用更新
-  - 添加商品识别、车辆识别（车辆细粒度分类、车辆 ReID）、logo 识别、动漫人物识别应用具体案例及相关模型
-  - 添加图像识别完整 pipeline，包含检测模块、特征提取模块、向量检索模块
-- 预测部署
-  - 添加百度自研向量检索模块 Mobius，支持图像识别系统预测部署
-  - 图像识别，建立特征库支持 batch_size>1
-- 文档更新
-  - 添加图像识别相关文档
-  - 修复之前文档 bug
-- 推荐库版本
-  - python 版本：3.7
-  - PaddlePaddle：2.1.2

docs/zh_CN/quick_start/index.rst

-快速开始
-================================
-
-.. toctree::
-   :maxdepth: 2
-
-   quick_start_classification_new_user.md
-   quick_start_classification_professional.md
-   quick_start_recognition.md
-   quick_start_multilabel_classification.md

docs/zh_CN/quick_start/quick_start_multilabel_classification.md

@@ -50,6 +50,10 @@ python3 -m paddle.distributed.launch \
 
 训练 10 epoch 之后，验证集最好的正确率应该在 0.95 左右。
 
+**注意:** 
+1. 目前多标签分类的损失函数仅支持`MultiLabelLoss`(BCE Loss)。
+2. 目前多标签分类的评估指标支持`AccuracyScore`和`HammingDistance`,其他评估指标敬请期待。
+
 <a name="3"></a>
 
 ## 3. 模型评估
@@ -71,7 +75,7 @@ python3 tools/infer.py \
 
 得到类似下面的输出：
 ```
-[{'class_ids': [6, 13, 17, 23, 26, 30], 'scores': [0.95683, 0.5567, 0.55211, 0.99088, 0.5943, 0.78767], 'file_name': './deploy/images/0517_2715693311.jpg', 'label_names': []}]
+[{'class_ids': [6, 13, 17, 23, 30], 'scores': [0.98217, 0.78129, 0.64377, 0.9942, 0.96109], 'label_names': ['clouds', 'lake', 'ocean', 'sky', 'water'], 'file_name': 'deploy/images/0517_2715693311.jpg'}]
 ```
 
 <a name="5"></a>
@@ -100,10 +104,13 @@ cd ./deploy
 
 ```
 python3 python/predict_cls.py \
-     -c configs/inference_multilabel_cls.yaml
+     -c configs/inference_cls_multilabel.yaml
 ```
+推理图片如下：
 
-得到类似下面的输出：
+![](../../images/quick_start/multi_label_demo.png)
+
+执行推理命令后，得到类似下面的输出：
 ```
-0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): []
+0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 30], score(s): [0.98, 0.78, 0.64, 0.99, 0.96], label_name(s): ['clouds', 'lake', 'ocean', 'sky', 'water']
 ```

docs/zh_CN/others/update_history.md → docs/zh_CN/version_history.md

+# 版本更新信息
+----------
+## 目录
+* [1. v2.3](#1)
+* [2. v2.2](#2)
+
+<a name='1'></a>
+
+## 1. v2.3
+
+- 模型更新
+  - 添加轻量化模型预训练权重，包括检测模型、特征模型
+  - 发布 PP-LCNet 系列模型，此系列模型是专门在 CPU 上设计运行的自研模型
+  - SwinTransformer、Twins、Deit 支持从 scrach 直接训练，达到论文精度
+- 框架基础能力
+  - 添加 DeepHash 模块，支持特征模型直接输出二值特征
+  - 添加 PKSampler，特征模型不能多机多卡的训练的问题
+  - 支持 PaddleSlim：支持分类模型、特征模型的量化、裁剪训练及离线量化功能
+  - Legendary models 支持模型中间结果输出
+  - 支持多标签分类训练
+- 预测部署
+  - 使用 Faiss 替换原有特征检索库，提升平台适配性
+  - 支持 PaddleServing：支持分类模型、图像识别流程的部署
+
+- 推荐库版本
+  - python 版本：3.7
+  - PaddlePaddle 版本：2.1.3
+  - PaddleSlim 版本：2.2.0
+  - PaddleServing 版本：0.6.1
+
+<a name='2'></a>
+
+## 2. v2.2
+
+- 模型更新
+  - 添加 LeViT、Twins、TNT、DLA、HardNet、RedNet、SwinTransfomer 模型
+- 框架基础能力
+  - 将分类模型分为两类
+    -  legendary models：引入 TheseusLayer 基类，及增加了修改网络功能接口，同时支持网络截断输出功能
+    - model zoo：其他普通分类模型
+  - 添加 Metric Learning 算法支持
+    - 添加多种相关 Loss 算法，及基础网络模块 gears（支持与 backbone、loss 组合）方便使用
+    - 同时支持普通分类及 metric learning 相关任务训练
+  - 支持静态图训练
+  - 分类训练支持 dali 加速
+  - 支持 fp16 训练
+- 应用更新
+  - 添加商品识别、车辆识别（车辆细粒度分类、车辆 ReID）、logo 识别、动漫人物识别应用具体案例及相关模型
+  - 添加图像识别完整 pipeline，包含检测模块、特征提取模块、向量检索模块
+- 预测部署
+  - 添加百度自研向量检索模块 Mobius，支持图像识别系统预测部署
+  - 图像识别，建立特征库支持 batch_size>1
+- 文档更新
+  - 添加图像识别相关文档
+  - 修复之前文档 bug
+- 推荐库版本
+  - python 版本：3.7
+  - PaddlePaddle：2.1.2
+
 # 更新日志
 
 - 2022.4.21 新增 CVPR2022 oral论文 [MixFormer](https://arxiv.org/pdf/2204.02557.pdf) 相关[代码](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files)。

ppcls/configs/quick_start/professional/MobileNetV1_multilabel.yaml

@@ -99,7 +99,7 @@ DataLoader:
       use_shared_memory: True
 
 Infer:
-  infer_imgs: ./deploy/images/0517_2715693311.jpg
+  infer_imgs: deploy/images/0517_2715693311.jpg
   batch_size: 10
   transforms:
     - DecodeImage:
@@ -116,9 +116,10 @@ Infer:
         order: ''
     - ToCHWImage:
   PostProcess:
-    name: MultiLabelTopk
-    topk: 5
-    class_id_map_file: None
+    name: MultiLabelThreshOutput
+    threshold: 0.5
+    class_id_map_file: "ppcls/utils/NUS-WIDE-SCENE_label_list.txt"
+    delimiter: " "
 
 Metric:
   Train:

ppcls/data/dataloader/pk_sampler.py

@@ -53,14 +53,14 @@ class PKSampler(DistributedBatchSampler):
             f"PKSampler configs error, sample_per_id({sample_per_id}) must be a divisor of batch_size({batch_size})."
         assert hasattr(self.dataset,
                        "labels"), "Dataset must have labels attribute."
-        self.sample_per_label = sample_per_id
+        self.sample_per_id = sample_per_id
         self.label_dict = defaultdict(list)
         self.sample_method = sample_method
         for idx, label in enumerate(self.dataset.labels):
             self.label_dict[label].append(idx)
         self.label_list = list(self.label_dict)
-        assert len(self.label_list) * self.sample_per_label > self.batch_size, \
-            "batch size should be smaller than "
+        assert len(self.label_list) * self.sample_per_id >= self.batch_size, \
+            f"batch size({self.batch_size}) should not be bigger than than #classes({len(self.label_list)})*sample_per_id({self.sample_per_id})"
         if self.sample_method == "id_avg_prob":
             self.prob_list = np.array([1 / len(self.label_list)] *
                                       len(self.label_list))
@@ -94,7 +94,7 @@ class PKSampler(DistributedBatchSampler):
                     format(diff))
 
     def __iter__(self):
-        label_per_batch = self.batch_size // self.sample_per_label
+        label_per_batch = self.batch_size // self.sample_per_id
         for _ in range(len(self)):
             batch_index = []
             batch_label_list = np.random.choice(
@@ -104,17 +104,17 @@ class PKSampler(DistributedBatchSampler):
                 p=self.prob_list)
             for label_i in batch_label_list:
                 label_i_indexes = self.label_dict[label_i]
-                if self.sample_per_label <= len(label_i_indexes):
+                if self.sample_per_id <= len(label_i_indexes):
                     batch_index.extend(
                         np.random.choice(
                             label_i_indexes,
-                            size=self.sample_per_label,
+                            size=self.sample_per_id,
                             replace=False))
                 else:
                     batch_index.extend(
                         np.random.choice(
                             label_i_indexes,
-                            size=self.sample_per_label,
+                            size=self.sample_per_id,
                             replace=True))
             if not self.drop_last or len(batch_index) == self.batch_size:
                 yield batch_index

ppcls/data/postprocess/__init__.py

@@ -16,9 +16,10 @@ import importlib
 
 from . import topk, threshoutput
 
-from .topk import Topk, MultiLabelTopk
-from .threshoutput import ThreshOutput
-from .attr_rec import VehicleAttribute, PersonAttribute, TableAttribute
+from .topk import Topk
+from .threshoutput import ThreshOutput, MultiLabelThreshOutput
+from .attr_rec import VehicleAttribute, PersonAttribute
+
 
 
 def build_postprocess(config):

ppcls/data/postprocess/threshoutput.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import numpy as np
 import paddle.nn.functional as F
 
 
@@ -34,3 +36,55 @@ class ThreshOutput(object):
                 result["file_name"] = file_names[idx]
             y.append(result)
         return y
+
+
+class MultiLabelThreshOutput(object):
+    def __init__(self, threshold=0.5, class_id_map_file=None, delimiter=None):
+        self.threshold = threshold
+        self.delimiter = delimiter if delimiter is not None else " "
+        self.class_id_map = self.parse_class_id_map(class_id_map_file)
+
+    def parse_class_id_map(self, class_id_map_file):
+        if class_id_map_file is None:
+            return None
+        if not os.path.exists(class_id_map_file):
+            print(
+                "Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
+            )
+            return None
+
+        try:
+            class_id_map = {}
+            with open(class_id_map_file, "r") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    partition = line.split("\n")[0].partition(self.delimiter)
+                    class_id_map[int(partition[0])] = str(partition[-1])
+        except Exception as ex:
+            print(ex)
+            class_id_map = None
+        return class_id_map
+
+    def __call__(self, x, file_names=None):
+        y = []
+        x = F.sigmoid(x).numpy()
+        for idx, probs in enumerate(x):
+            index = np.where(probs >= self.threshold)[0].astype("int32")
+            clas_id_list = []
+            score_list = []
+            label_name_list = []
+            for i in index:
+                clas_id_list.append(i.item())
+                score_list.append(probs[i].item())
+                if self.class_id_map is not None:
+                    label_name_list.append(self.class_id_map[i.item()])
+            result = {
+                "class_ids": clas_id_list,
+                "scores": np.around(
+                    score_list, decimals=5).tolist(),
+                "label_names": label_name_list    
+            }
+            if file_names is not None:
+                result["file_name"] = file_names[idx]
+            y.append(result)
+        return y

ppcls/data/postprocess/topk.py

@@ -46,19 +46,18 @@ class Topk(object):
             class_id_map = None
         return class_id_map
 
-    def __call__(self, x, file_names=None, multilabel=False):
+    def __call__(self, x, file_names=None):
         if isinstance(x, dict):
             x = x['logits']
         assert isinstance(x, paddle.Tensor)
         if file_names is not None:
             assert x.shape[0] == len(file_names)
-        x = F.softmax(x, axis=-1) if not multilabel else F.sigmoid(x)
+        x = F.softmax(x, axis=-1)
         x = x.numpy()
         y = []
         for idx, probs in enumerate(x):
             index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
-                "int32") if not multilabel else np.where(
-                    probs >= 0.5)[0].astype("int32")
+                "int32")
             clas_id_list = []
             score_list = []
             label_name_list = []
@@ -79,10 +78,3 @@ class Topk(object):
             y.append(result)
         return y
 
-
-class MultiLabelTopk(Topk):
-    def __init__(self, topk=1, class_id_map_file=None):
-        super().__init__()
-
-    def __call__(self, x, file_names=None):
-        return super().__call__(x, file_names, multilabel=True)

ppcls/engine/engine.py

@@ -502,7 +502,7 @@ class Engine(object):
         assert self.mode == "export"
         use_multilabel = self.config["Global"].get(
             "use_multilabel",
-            False) and "ATTRMetric" in self.config["Metric"]["Eval"][0]
+            False) or "ATTRMetric" in self.config["Metric"]["Eval"][0]
         model = ExportModel(self.config["Arch"], self.model, use_multilabel)
         if self.config["Global"]["pretrained_model"] is not None:
             load_dygraph_pretrain(model.base_model,

ppcls/utils/NUS-WIDE-SCENE_label_list.txt

+0 airport
+1 beach
+2 bridge
+3 buildings
+4 castle
+5 cityscape
+6 clouds
+7 frost
+8 garden
+9 glacier
+10 grass
+11 harbor
+12 house
+13 lake
+14 moon
+15 mountain
+16 nighttime
+17 ocean
+18 plants
+19 railroad
+20 rainbow
+21 reflection
+22 road
+23 sky
+24 snow
+25 street
+26 sunset
+27 temple
+28 town
+29 valley
+30 water
+31 waterfall
+32 window

test_tipc/README.md

@@ -115,3 +115,4 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
 - [test_serving_infer_python 使用](docs/test_serving_infer_python.md)：测试python serving功能。
 - [test_serving_infer_cpp 使用](docs/test_serving_infer_cpp.md)：测试cpp serving功能。
 - [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md)：测试基于Python的多机多卡训练与推理等基本功能。
+- [benchmark_train 使用](./docs/benchmark_train.md)：测试基于Python的训练benchmark等基本功能。

test_tipc/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base_train_infer_python.txt

@@ -51,7 +51,7 @@ inference:python/predict_rec.py -c configs/inference_rec.yaml
 null:null
 null:null
 ===========================train_benchmark_params==========================
-batch_size:256
+batch_size:128
 fp_items:fp32|fp16
 epoch:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile