-****A High-Efficient Development Toolkit for Object Detection based on [PaddlePaddle](https://github.com/paddlepaddle/paddle).****
+**A High-Efficient Development Toolkit for Object Detection based on [PaddlePaddle](https://github.com/paddlepaddle/paddle)**
@@ -13,70 +13,71 @@ English | [简体中文](README_cn.md)
-
+
+
- - Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), supporting s/m/l/x version, achieving mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, supporting AMP training and its training speed is 33% faster than PP-YOLOv2.
- - Release enhanced models of [PP-PicoDet](configs/picodet), including PP-PicoDet-XS model with 0.7M parameters, its mAP promoted ~2% on COCO, inference speed accelerated 63% on CPU, and post-processing integrated into the network to optimize deployment pipeline.
- - Release real-time human analysis tool [PP-Human](deploy/pipeline), which is based on data from real-life situations, supporting pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics and action recognition.
- - Release [YOLOX](configs/yolox), supporting nano/tiny/s/m/l/x version, achieving mAP as 51.8% on COCO val dataset by YOLOX-x.
+
-- 2021.11.03: Release [release/2.3](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.3) version. Release mobile object detection model ⚡[PP-PicoDet](configs/picodet), mobile keypoint detection model ⚡[PP-TinyPose](configs/keypoint/tiny_pose)，Real-time tracking system [PP-Tracking](deploy/pptracking). Release object detection models, including [Swin-Transformer](configs/faster_rcnn), [TOOD](configs/tood), [GFL](configs/gfl), release [Sniper](configs/sniper) tiny object detection models and optimized [PP-YOLO-EB](configs/ppyolo) model for EdgeBoard. Release mobile keypoint detection model [Lite HRNet](configs/keypoint).
+## Product Update
-- 2021.08.10: Release [release/2.2](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.2) version. Release Transformer object detection models, including [DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn). Release [keypoint detection](configs/keypoint) models, including DarkHRNet and model trained on MPII dataset. Release [head-tracking](configs/mot/headtracking21) and [vehicle-tracking](configs/mot/vehicle) multi-object tracking models.
+- 🔥 **2022.7.14：Release [pedestrian analysis tool PP-Human v2](./deploy/pipeline)**
+ - Four major functions: five complicated action recognition with high performance and Flexible, real-time human attribute recognition, visitor flow statistics and high-accuracy multi-camera tracking.
+ - High performance algorithm: including pedestrian detection, tracking, attribute recognition which is robust to the number of targets and the variant of background and light.
+ - Highly Flexible: providing complete introduction of end-to-end development and optimization strategy, simple command for deployment and compatibility with different input format.
-- 2021.05.20: Release [release/2.1](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.1) version. Release [Keypoint Detection](configs/keypoint), including HigherHRNet and HRNet, [Multi-Object Tracking](configs/mot), including DeepSORT，JDE and FairMOT. Release model compression for PPYOLO series models.Update documents such as [EXPORT ONNX MODEL](deploy/EXPORT_ONNX_MODEL.md).
+- 2022.3.24：PaddleDetection released[release/2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
+ - Release high-performanace SOTA object detection model [PP-YOLOE](configs/ppyoloe). It integrates cloud and edge devices and provides S/M/L/X versions. In particular, Verson L has the accuracy as 51.4% on COCO test 2017 dataset, inference speed as 78.1 FPS on a single Test V100. It supports mixed precision training, 33% faster than PP-YOLOv2. Its full range of multi-sized models can meet different hardware arithmetic requirements, and adaptable to server, edge-device GPU and other AI accelerator cards on servers.
+ - Release ultra-lightweight SOTA object detection model [PP-PicoDet Plus](configs/picodet) with 2% improvement in accuracy and 63% improvement in CPU inference speed. Add PicoDet-XS model with a 0.7M parameter, providing model sparsification and quantization functions for model acceleration. No specific post processing module is required for all the hardware, simplifying the deployment.
+ - Release the real-time pedestrian analysis tool [PP-Human](deploy/pphuman). It has four major functions: pedestrian tracking, visitor flow statistics, human attribute recognition and falling detection. For falling detection, it is optimized based on real-life data with accurate recognition of various types of falling posture. It can adapt to different environmental background, light and camera angle.
+ - Add [YOLOX](configs/yolox) object detection model with nano/tiny/S/M/L/X. X version has the accuracy as 51.8% on COCO Val2017 dataset.
-## Introduction
+- 2021.11.03: PaddleDetection released [release/2.3 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3)
-PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular design with configurable modules such as network components, data augmentations and losses. It releases many kinds SOTA industry practice models and integrates abilities of model compression and cross-platform high-performance deployment to help developers in the whole process with a faster and better way.
+ - Release light-weight featured detection model ⚡[PP-PicoDet](configs/picodet). With a 0.99m parameter, its inference speed could reach to 150FPS when COCO mAP as over 30%
+ - Release light-weight keypoint special model ⚡[PP-TinyPose](configs/keypoint/tiny_pose), FP16 inference speed as 122 FPS and on a single person detection. It has high performance and fast speed, unlimited detection headcounts while being effective on small objects.
+ - Release real-time tracking system [PP-Tracking](deploy/pptracking), covering pedestrian, vehicle and multi-category tracking with single and multi-camera, optimization for small and intensive objects, providing technical solutions for human and vehicle traffic.
+ - Add object detection models [Swin Transformer](configs/faster_rcnn)，[TOOD](configs/tood)，[GFL](configs/gfl)
+ - Release optimized small object detection model [Sniper](configs/sniper) and [PP-YOLO-EB](configs/ppyolo) model which optimized for EdgeBoard
+ - Add light-weight keypoint model [Lite HRNet](configs/keypoint) and supported Paddle Lite deployment
-#### PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc.
+- [More releases](https://github.com/PaddlePaddle/PaddleDetection/releases)
-
-
-
+## Brief Introduction
-#### PaddleDetection covers industrialization, smart city, security & protection, retail, medicare industry and etc.
+**PaddleDetection** is an end-to-end object detection development kit based on PaddlePaddle. Providing **over 30 model algorithm** and **over 250 pre-trained models**, it covers object detection, instance segmentation, keypoint detection, multi-object tracking. In particular, PaddleDetection offers **high- performance & light-weight** industrial SOTA models on **servers and mobile** devices, champion solution and cutting-edge algorithm. PaddleDetection provides various data augmentation methods, configurable network components, loss functions and other advanced optimization & deployment schemes. In addition to running through the whole process of data processing, model development, training, compression and deployment, PaddlePaddle also provides rich cases and tutorials to accelerate the industrial application of algorithm.
-## Features
-
-- **Rich Models**
-
- PaddleDetection provides rich of models, including **250+ pre-trained models** such as **object detection**, **instance segmentation**, **face detection**, **keypoint detection**, **multi-object tracking** and etc, covering a variety of **global competition champion** schemes.
-- **Highly Flexible**
- Components are designed to be modular. Model architectures, as well as data preprocess pipelines and optimization strategies, can be easily customized with simple configuration changes.
-
-- **Production Ready**
-
- From data augmentation, constructing models, training, compression, depolyment, get through end to end, and complete support for multi-architecture, multi-device deployment for **cloud and edge device**.
+## Features
-- **High Performance**
+- **Rich model library**: PaddleDetection provides over 250 pre-trained models including **object detection, instance segmentation, face recognition, multi-object tracking**. It covers a variety of **global competition champion** schemes.
+- **Simple to use**: Modular design, decoupling each network component, easy for developers to build and try various detection models and optimization strategies, quick access to high-performance, customized algorithm.
+- **Getting Through End to End**: PaddlePaddle gets through end to end from data augmentation, constructing models, training, compression, depolyment. It also supports multi-architecture, multi-device deployment for **cloud and edge** device.
+- **High Performance**: Due to the high performance core, PaddlePaddle has clear advantages in training speed and memory occupation. It also supports FP16 training and multi-machine training.
- Based on the high performance core of PaddlePaddle, advantages of training speed and memory occupation are obvious. FP16 training and multi-machine training are supported as well.
+
+
+
Community
+## Exchanges
-- If you have any problem or suggestion on PaddleDetection, please send us issues through [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues).
+- If you have any question or suggestion, please give us your valuable input via [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues)
-- Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").
+ Welcome to join PaddleDetection user groups on QQ, WeChat (scan the QR code, add and reply "D" to the assistant)
-## Overview of Kit Structures
+## Kit Structure
@@ -97,117 +98,127 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl
-
Object Detection
+ Object Detection
Faster RCNN
FPN
Cascade-RCNN
-
Libra RCNN
-
Hybrid Task RCNN
PSS-Det
RetinaNet
-
YOLOv3
-
YOLOv4
+
YOLOv3
PP-YOLOv1/v2
PP-YOLO-Tiny
PP-YOLOE
YOLOX
SSD
-
CornerNet-Squeeze
+
CenterNet
FCOS
TTFNet
+
TOOD
+
GFL
PP-PicoDet
DETR
Deformable DETR
Swin Transformer
Sparse RCNN
-
-
Instance Segmentation
-
+
+ Instance Segmentation
+
Mask RCNN
+
Cascade Mask RCNN
SOLOv2
-
-
Face Detection
+
+ Face Detection
-
FaceBoxes
BlazeFace
-
BlazeFace-NAS
-
-
Multi-Object-Tracking
+
+ Multi-Object-Tracking
JDE
FairMOT
DeepSORT
-
-
KeyPoint-Detection
+
ByteTrack
+
+ KeyPoint-Detection
HRNet
HigherHRNet
-
+
Lite-HRNet
+
PP-TinyPose
+
+ Details
ResNet(&vd)
-
ResNeXt(&vd)
+
Res2Net(&vd)
+
CSPResNet
SENet
Res2Net
HRNet
-
Hourglass
-
CBNet
-
GCNet
+
Lite-HRNet
DarkNet
CSPDarkNet
-
VGG
MobileNetv1/v3
+
ShuffleNet
GhostNet
-
Efficientnet
-
BlazeNet
-
+
BlazeNet
+
DLA
+
HardNet
+
LCNet
+
ESNet
+
Swin-Transformer
+
-
Common
+ Common
Sync-BN
Group Norm
DCNv2
-
Non-local
-
+
EMA
+
-
KeyPoint
+ KeyPoint
DarkPose
-
+
-
FPN
+ FPN
BiFPN
-
BFP
+
CSP-PAN
+
Custom-PAN
+
ES-PAN
HRFPN
-
ACFPN
-
+
-
Loss
+ Loss
Smooth-L1
GIoU/DIoU/CIoU
IoUAware
-
+
Focal Loss
+
CT Focal Loss
+
VariFocal Loss
+
-
Post-processing
+ Post-processing
SoftNMS
MatrixNMS
-
+
-
Speed
+ Speed
FP16 training
Multi-machine training
-
+
+ Details
Resize
Lighting
@@ -217,13 +228,13 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl
Color Distort
Random Erasing
Mixup
-
Mosaic
AugmentHSV
+
Mosaic
Cutmix
Grid Mask
Auto Augment
Random Perspective
-
+
@@ -232,139 +243,242 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl
-## Overview of Model Performance
+## Model Performance
-The relationship between COCO mAP and FPS on Tesla V100 of representative models of each server side architectures and backbones.
+
+ Performance comparison of Cloud models
+
+The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
-**NOTE:**
-
-- `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
-
-- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
+**Clarification：**
-- `PP-YOLO` achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass [YOLOv4](https://arxiv.org/abs/2004.10934)
+- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
+- `Cascade-Faster-RCNN`stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
+- `PP-YOLO` reached accuracy as 45.9% on COCO dataset, inference speed as 72.9 FPS on Tesla V100, higher than [YOLOv4]([[2004.10934] YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/abs/2004.10934)) in terms of speed and accuracy
+- `PP-YOLO v2`are optimized `PP-YOLO`. It reached accuracy as 49.5% on COCO dataset, inference speed as 68.9 FPS on Tesla V100.
+- `PP-YOLOE`are optimized `PP-YOLO v2`. It reached accuracy as 51.4% on COCO dataset, inference speed as 78.1 FPS on Tesla V100
+- The models in the figure are available in the[ model library](#模型库)
-- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
+
-- `PP-YOLOE` is optimized version of `PP-YOLO v2` which has mAP of 51.6% and 78.1FPS on Tesla V100
+
+ Performance omparison on mobiles
-- All these models can be get in [Model Zoo](#ModelZoo)
-
-The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models.
+The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices.
-**NOTE:**
-
-- All data tested on Qualcomm Snapdragon 865(4*A77 + 4*A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
-- [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are developed and released by PaddleDetection, other models are not provided in PaddleDetection.
-
-## Tutorials
-
-### Get Started
-
-- [Installation Guide](docs/tutorials/INSTALL.md)
-- [Prepare Dataset](docs/tutorials/PrepareDataSet_en.md)
-- [Quick Start on PaddleDetection](docs/tutorials/GETTING_STARTED.md)
-
-### Advanced Tutorials
-
-- Parameter Configuration
-
- - [Parameter configuration for RCNN model](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md)
- - [Parameter configuration for PP-YOLO model](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation_en.md)
-
-- Model Compression(Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
-
- - [Prune/Quant/Distill](configs/slim)
-
-- [Metric Logging during Model Training](docs/tutorials/logging_en.md)
-
-- Inference and Deployment
-
- - [Export model for inference](deploy/EXPORT_MODEL_en.md)
- - [Paddle Inference](deploy/README_en.md)
- - [Python inference](deploy/python)
- - [C++ inference](deploy/cpp)
- - [Paddle-Lite](deploy/lite)
- - [Paddle Serving](deploy/serving)
- - [Export ONNX model](deploy/EXPORT_ONNX_MODEL_en.md)
- - [Inference benchmark](deploy/BENCHMARK_INFER_en.md)
- - [Exporting to ONNX and using OpenVINO for inference](docs/advanced_tutorials/openvino_inference/README.md)
-
-- Advanced Development
-
- - [New data augmentations](docs/advanced_tutorials/READER_en.md)
- - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md)
-
-## Model Zoo
-
-- General Object Detection
- - [Model library and baselines](docs/MODEL_ZOO_cn.md)
- - [PP-YOLOE](configs/ppyoloe/README_cn.md)
- - [PP-YOLO](configs/ppyolo/README.md)
- - [PP-PicoDet](configs/picodet/README.md)
- - [Enhanced Anchor Free model--TTFNet](configs/ttfnet/README_en.md)
- - [Mobile models](static/configs/mobile/README_en.md)
- - [676 classes of object detection](static/docs/featured_model/LARGE_SCALE_DET_MODEL_en.md)
- - [Two-stage practical PSS-Det](configs/rcnn_enhance/README_en.md)
- - [SSLD pretrained models](docs/feature_models/SSLD_PRETRAINED_MODEL_en.md)
-- General Instance Segmentation
- - [SOLOv2](configs/solov2/README.md)
-- Rotated Object Detection
- - [S2ANet](configs/dota/README_en.md)
-- [Keypoint Detection](configs/keypoint)
- - [PP-TinyPose](configs/keypoint/tiny_pose)
- - HigherHRNet
- - HRNet
- - LiteHRNet
-- [Multi-Object Tracking](configs/mot/README.md)
- - [PP-Tracking](deploy/pptracking/README_en.md)
- - [DeepSORT](configs/mot/deepsort/README.md)
- - [JDE](configs/mot/jde/README.md)
- - [FairMOT](configs/mot/fairmot/README.md)
- - [ByteTrack](configs/mot/bytetrack/README.md)
-- Practical Specific Models
- - [Face detection](configs/face_detection/README_en.md)
- - [Pedestrian detection](configs/pedestrian/README.md)
- - [Vehicle detection](configs/vehicle/README.md)
-- Scienario Solution
- - [Real-Time Human Analysis Tool PP-Human](deploy/pipeline)
-- Competition Solution
- - [Objects365 2019 Challenge champion model](static/docs/featured_model/champion_model/CACascadeRCNN_en.md)
- - [Best single model of Open Images 2019-Object Detection](static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL_en.md)
+**Clarification：**
+
+- Tests were conducted on Qualcomm Snapdragon 865 (4 \*A77 + 4 \*A55) batch_size=1, 4 thread, and NCNN inference library, test script see [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
+- [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are self-developed models of PaddleDetection, and other models are not tested yet.
+
+
+
+## Model libraries
+
+
+ 1. General detection
+
+#### PP-YOLOE series Recommended scenarios: Cloud GPU such as Nvidia V100, T4 and edge devices such as Jetson series
+
+| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 Speed(FPS) | Configuration | Download |
+|:---------- |:------------------:|:-----------------------------:|:-------------------------------------------------------:|:----------------------------------------------------------------------------------------:|
+| PP-YOLOE-s | 42.7 | 333.3 | [Link](configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) |
+| PP-YOLOE-m | 48.6 | 208.3 | [Link](configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_2x_coco.pdparams) |
+| PP-YOLOE-l | 50.9 | 149.2 | [Link](configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) |
+| PP-YOLOE-x | 51.9 | 95.2 | [Link](configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams) |
+
+#### PP-PicoDet series Recommended scenarios: Mobile chips and x86 CPU devices, such as ARM CPU(RK3399, Raspberry Pi) and NPU(BITMAIN)
+
+| Model | COCO Accuracy（mAP） | Snapdragon 865 four-thread speed (ms) | Configuration | Download |
+|:---------- |:------------------:|:-------------------------------------:|:-----------------------------------------------------:|:-------------------------------------------------------------------------------------:|
+| PicoDet-XS | 23.5 | 7.81 | [Link](configs/picodet/picodet_xs_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_xs_320_coco_lcnet.pdparams) |
+| PicoDet-S | 29.1 | 9.56 | [Link](configs/picodet/picodet_s_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams) |
+| PicoDet-M | 34.4 | 17.68 | [Link](configs/picodet/picodet_m_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco_lcnet.pdparams) |
+| PicoDet-L | 36.1 | 25.21 | [Link](configs/picodet/picodet_l_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco_lcnet.pdparams) |
+
+#### Frontier detection algorithm
+
+| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 speed(FPS) | Configuration | Download |
+|:-------- |:------------------:|:-----------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------:|
+| YOLOX-l | 50.1 | 107.5 | [Link](configs/yolox/yolox_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) |
+| YOLOv5-l | 48.6 | 136.0 | [Link](https://github.com/nemonameless/PaddleDetection_YOLOv5/blob/main/configs/yolov5/yolov5_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) |
+
+#### Other general purpose models [doc](docs/MODEL_ZOO_cn.md)
+
+
+
+
+ 2. Instance segmentation
+
+| Model | Introduction | Recommended Scenarios | COCO Accuracy(mAP) | Configuration | Download |
+|:----------------- |:-------------------------------------------------------- |:--------------------------------------------- |:--------------------------------:|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|
+| Mask RCNN | Two-stage instance segmentation algorithm |
## 方案说明
diff --git a/deploy/pipeline/docs/tutorials/mtmct.md b/deploy/pipeline/docs/tutorials/mtmct.md
index 2236d9355dce05698b492f778622450c5db3ac16..d0835719b92aefded004ff95b594d18e77b4a9a4 100644
--- a/deploy/pipeline/docs/tutorials/mtmct.md
+++ b/deploy/pipeline/docs/tutorials/mtmct.md
@@ -7,7 +7,7 @@ PP-Human跨镜头跟踪模块主要目的在于提供一套简洁、高效的跨
## 使用方法
-1. 下载模型 [REID模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) 并解压到```./output_inference```路径下，修改配置文件中模型路径。也可简单起见直接用默认配置，自动下载模型。 MOT模型请参考[mot说明](./mot.md)文件下载。
+1. 下载模型 [行人跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)和[REID模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) 并解压到```./output_inference```路径下，修改配置文件中模型路径。也可简单起见直接用默认配置，自动下载模型。 MOT模型请参考[mot说明](./mot.md)文件下载。
2. 跨镜头跟踪模式下，要求输入的多个视频放在同一目录下，同时开启infer_cfg_pphuman.yml 中的REID选择中的enable=True, 命令如下：
```python
diff --git a/docs/advanced_tutorials/customization/action_recognotion/README.md b/docs/advanced_tutorials/customization/action_recognotion/README.md
index 99a2e52059760660f7fa239442ee0ce767c5684d..4664715c32985a33aa8045a445eacd27651d60cd 100644
--- a/docs/advanced_tutorials/customization/action_recognotion/README.md
+++ b/docs/advanced_tutorials/customization/action_recognotion/README.md
@@ -1,31 +1,25 @@
# 行为识别任务二次开发
-在产业落地过程中应用行为识别算法，不可避免地会出现希望自定义类型的行为识别的需求，或是对已有行为识别模型的优化，以提升在特定场景下模型的效果。鉴于行为的多样性，PP-Human支持抽烟、打电话、摔倒、打架、人员闯入五种异常行为识别，并根据行为的不同，集成了基于视频分类、基于检测、基于图像分类以及基于骨骼点的四种行为识别技术方案，可覆盖90%+动作类型的识别，满足各类开发需求。我们在本文档通过案例来介绍如何根据期望识别的行为来进行行为识别方案的选择，以及使用PaddleDetection进行行为识别算法二次开发工作，包括：方案选择、数据准备、模型优化思路和新增行为的开发流程。
+在产业落地过程中应用行为识别算法，不可避免地会出现希望自定义类型的行为识别的需求，或是对已有行为识别模型的优化，以提升在特定场景下模型的效果。鉴于行为的多样性，PP-Human支持抽烟、打电话、摔倒、打架、人员闯入五种异常行为识别，并根据行为的不同，集成了基于视频分类、基于检测、基于图像分类、基于跟踪以及基于骨骼点的五种行为识别技术方案，可覆盖90%+动作类型的识别，满足各类开发需求。我们在本文档通过案例来介绍如何根据期望识别的行为来进行行为识别方案的选择，以及使用PaddleDetection进行行为识别算法二次开发工作，包括：方案选择、数据准备、模型优化思路和新增行为的开发流程。
## 方案选择
在PaddleDetection的PP-Human中，我们为行为识别提供了多种方案：基于视频分类、基于图像分类、基于检测、以及基于骨骼点的行为识别方案，以期望满足不同场景、不同目标行为的需求。对于二次开发，首先我们需要确定要采用何种方案来实现行为识别的需求，其核心是要通过对场景和具体行为的分析、并考虑数据采集成本等因素，综合选择一个合适的识别方案。我们在这里简要列举了当前PaddleDetection中所支持的方案的优劣势和适用场景，供大家参考。
-| 技术方案 | 方案说明 | 方案优势 | 方案劣势 | 适用场景 |
-| :--: | :--: | :--: | :--: | :--: |
-| 基于人体骨骼点的行为识别 | 1. 通过目标检测技术识别出图像中的人； 2. 针对每个人，基于关键点检测技术识别出关键点； 3. 基于关键点序列变化识别出具体行为。 | 1. 可识别出每个人的行为； 2. 聚焦动作本身，鲁棒性和泛化性好； | 1. 对关键点检测依赖较强，人员较密集或存在遮挡等情况效果不佳； 2. 无法准确识别多人交互动作； 3. 难以处理需要外观及场景信息的动作； 4. 数据收集和标注困难； | 适用于根据人体结构关键点能够区分的行为，背景简单，人数不多场景，如健身场景。 |
-| 基于人体id的分类 | 1. 通过目标检测技术得到图像中的人； 2. 针对每个人通过图像分类技术得到具体的行为类别。 | 1.通过检测技术可以为分类剔除无关背景的干扰，提升最终识别精度； 2. 方案简单，易于训练； 3. 数据采集容易； 4. 可结合跳帧及结果复用逻辑，速度快； | 1. 缺少时序信息； 2. 精度不高； | 对时序信息要求不强的动作，且动作既可通过人也可通过人+物的方式判断，如打电话。 |
-| 基于人体id的检测 | 1. 通过目标检测技术得到画面中的人； 2. 根据检测结果将人物从原图中抠出，再在扣得的图像中再次用目标检测技术检测与行为强相关的目标。 | 1. 方案简单，易于训练； 2. 可解释性强； 3. 数据采集容易； 4. 可结合跳帧及结果复用逻辑，速度快； | 1. 缺少时序信息； 2. 分辨率较低情况下效果不佳； 3. 密集场景容易发生动作误匹配 | 行为与某特定目标强相关的场景，且目标较小，需要两级检测才能准确定位，如吸烟。 |
-| 基于视频分类的行为识别 | 应用视频分类技术对整个视频场景进行分类。 | 1.充分利用背景上下文和时序信息； 2. 可利用语音、字幕等多模态信息； 3. 不依赖检测及跟踪模型； 4. 可处理多人共同组成的动作； | 1. 无法定位到具体某个人的行为； 2. 场景泛化能力较弱； 3.真实数据采集困难； | 无需具体到人的场景的判定，即判断是否存在某种特定行为，多人或对背景依赖较强的动作，如监控画面中打架识别等场景。 |
-
+
下面以PaddleDetection目前已经支持的几个具体动作为例，介绍每个动作方案的选型依据：
### 吸烟
-方案选择：基于人体id的检测
+方案选择：基于人体id检测的行为识别
原因：吸烟动作中具有香烟这个明显特征目标，因此我们可以认为当在某个人物的对应图像中检测到香烟时，该人物即在吸烟动作中。相比于基于视频或基于骨骼点的识别方案，训练检测模型需要采集的是图片级别而非视频级别的数据，可以明显减轻数据收集与标注的难度。此外，目标检测任务具有丰富的预训练模型资源，整体模型的效果会更有保障，
### 打电话
-方案选择：基于人体id的分类
+方案选择：基于人体id分类的行为识别
原因：打电话动作中虽然有手机这个特征目标，但为了区分看手机等动作，以及考虑到在安防场景下打电话动作中会出现较多对手机的遮挡（如手对手机的遮挡、人头对手机的遮挡等等），不利于检测模型正确检测到目标。同时打电话通常持续的时间较长，且人物本身的动作不会发生太大变化，因此可以因此采用帧级别图像分类的策略。
此外，打电话这个动作主要可以通过上半身判别，可以采用半身图片，去除冗余信息以降低模型训练的难度。
@@ -36,6 +30,12 @@
原因：摔倒是一个明显的时序行为的动作，可由一个人物本身进行区分，具有场景无关的特性。由于PP-Human的场景定位偏向安防监控场景，背景变化较为复杂，且部署上需要考虑到实时性，因此采用了基于骨骼点的行为识别方案，以获得更好的泛化性及运行速度。
+### 闯入
+
+方案选择：基于人体id跟踪的行为识别
+
+原因：闯入识别判断行人的路径或所在位置是否在某区域内即可，与人体自身动作无关，因此只需要跟踪人体跟踪结果分析是否存在闯入行为。
+
### 打架
方案选择：基于视频分类的行为识别
@@ -45,7 +45,8 @@
下面详细展开四大类方案的数据准备、模型优化和新增行为识别方法
-1. [基于人体id的检测](./idbased_det.md)
-2. [基于人体id的分类](./idbased_clas.md)
+1. [基于人体id检测的行为识别](./idbased_det.md)
+2. [基于人体id分类的行为识别](./idbased_clas.md)
3. [基于人体骨骼点的行为识别](./skeletonbased_rec.md)
-4. [基于视频分类的行为识别](./videobased_rec.md)
+4. [基于人体id跟踪的行为识别](../mot.md)
+5. [基于视频分类的行为识别](./videobased_rec.md)
diff --git a/docs/advanced_tutorials/openvino_inference/README.md b/docs/advanced_tutorials/openvino_inference/README.md
index 0a67651d9baadf6708adcda21628f9a1dcaaf31c..ac372eaa30d1f02a5016b42b928e9c56bfea547a 100644
--- a/docs/advanced_tutorials/openvino_inference/README.md
+++ b/docs/advanced_tutorials/openvino_inference/README.md
@@ -3,9 +3,9 @@
## Introduction
PaddleDetection has been a vibrant open-source project and has a large amout of contributors and maintainers around it. It is an AI framework which enables developers to quickly integrate AI capacities into their own projects and applications.
-Intel OpenVINO is a widely used free toolkit. It facilitates the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware.
+Intel OpenVINO is a widely used free toolkit. It facilitates the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware.
-Apparently, the upstream(Paddle) and the downstream(Intel OpenVINO) can work together to streamline and simplify the process of developing an AI model and deploying the model onto hardware, which, in turn, makes our lives easier.
+Apparently, the upstream(Paddle) and the downstream(Intel OpenVINO) can work together to streamline and simplify the process of developing an AI model and deploying the model onto hardware, which, in turn, makes our lives easier.
This article will show you how to use a PaddleDetection model [FairMOT](../../../configs/mot/fairmot/README.md) from the Model Zoo in PaddleDetection and use it with OpenVINO to do the inference.
@@ -50,7 +50,7 @@ Once the Paddle model has been converted to ONNX format, we can then use it with
1. ### Get the execution network
-So the 1st thing to do here is to get an execution network which can be used later to do the inference.
+So the 1st thing to do here is to get an execution network which can be used later to do the inference.
Here is the code.
@@ -70,7 +70,7 @@ Every AI model has its own steps of preprocessing, let's have a look how to do i
```
def prepare_input():
transforms = [
- T.Resize(target_size=(target_width, target_height)),
+ T.Resize(target_size=(target_width, target_height)),
T.Normalize(mean=(0,0,0), std=(1,1,1))
]
img_file = root_path / "images/street.jpeg"
@@ -87,7 +87,7 @@ def prepare_input():
3. ### Prediction
-After we have done all the load network and preprocessing, it finally comes to the stage of prediction.
+After we have done all the load network and preprocessing, it finally comes to the stage of prediction.
```
@@ -100,7 +100,7 @@ You might be surprised to see the very exciting stage this small. Hang on there,
4. ### Post-processing
-MOT(Multi-Object Tracking) is special, not like other AI models which require a few steps of post-processing. Instead, FairMOT requires a special object called tracker, to handle the prediction results. The prediction results are prediction detections and prediction embeddings.
+MOT(Multi-Object Tracking) is special, not like other AI models which require a few steps of post-processing. Instead, FairMOT requires a special object called tracker, to handle the prediction results. The prediction results are prediction detections and prediction embeddings.
Luckily, PaddleDetection has made this procesure easy for us, it has exported the JDETracker from `ppdet`, so that we do not need to write much code to handle it.
@@ -156,4 +156,4 @@ So these are the all steps which you need to follow in order to run FairMOT on y
A companion article which explains in details of this procedure will be released soon and a link to that article will be updated here soon.
-To see the full code, please take a look at [Paddle OpenVINO Prediction](docs/advanced_tutorials/openvino_inference/fairmot_onnx_openvino.py).
\ No newline at end of file
+To see the full code, please take a look at [Paddle OpenVINO Prediction](./fairmot_onnx_openvino.py).
diff --git a/docs/advanced_tutorials/openvino_inference/README_cn.md b/docs/advanced_tutorials/openvino_inference/README_cn.md
index 2fc001757157501bccaeffc37d900cbc6d31e7eb..aaaf84eb05c26359fcc48cb14a3f6104bd834d5d 100644
--- a/docs/advanced_tutorials/openvino_inference/README_cn.md
+++ b/docs/advanced_tutorials/openvino_inference/README_cn.md
@@ -68,7 +68,7 @@ def get_net():
```
def prepare_input():
transforms = [
- T.Resize(target_size=(target_width, target_height)),
+ T.Resize(target_size=(target_width, target_height)),
T.Normalize(mean=(0,0,0), std=(1,1,1))
]
img_file = root_path / "images/street.jpeg"
@@ -93,7 +93,7 @@ def predict(exec_net, input):
return result
```
-您可能会惊讶地看到, 最激动人心的步骤居然如此简单。 不过下一个阶段会加复杂。
+您可能会惊讶地看到, 最激动人心的步骤居然如此简单。 不过下一个阶段会更加复杂。
4. ### 后处理
@@ -138,7 +138,7 @@ def postprocess(pred_dets, pred_embs, threshold = 0.5):
5. ### 画出检测框(可选)
-这一步是可选的。出于演示目的，我只使用 `plot_tracking_dict()` 方法在图像上绘制所有边界框。 但是，如果您没有相同的要求，则不需要这样做。
+这一步是可选的。出于演示目的，我只使用 `plot_tracking_dict()` 方法在图像上绘制所有边界框。 但是，如果您没有相同的要求，则不需要这样做。
```
online_im = plot_tracking_dict(
@@ -154,4 +154,4 @@ online_im = plot_tracking_dict(
之后会有一篇详细解释此过程的配套文章将会发布，并且该文章的链接将很快在此处更新。
-完整代码请查看 [Paddle OpenVINO 预测](docs/advanced_tutorials/openvino_inference/fairmot_onnx_openvino.py).
\ No newline at end of file
+完整代码请查看 [Paddle OpenVINO 预测](./fairmot_onnx_openvino.py).
diff --git a/static/LICENSE b/static/LICENSE
deleted file mode 100644
index 261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64..0000000000000000000000000000000000000000
--- a/static/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/static/README.md b/static/README.md
deleted file mode 120000
index 4015683cfa5969297febc12e7ca1264afabbc0b5..0000000000000000000000000000000000000000
--- a/static/README.md
+++ /dev/null
@@ -1 +0,0 @@
-README_cn.md
\ No newline at end of file
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-简体中文 | [English](README_en.md)
-
-文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
-
-# 简介
-
-PaddleDetection飞桨目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。
-
-PaddleDetection模块化地实现了多种主流目标检测算法，提供了丰富的数据增强策略、网络模块组件（如骨干网络）、损失函数等，并集成了模型压缩和跨平台高性能部署能力。
-
-经过长时间产业实践打磨，PaddleDetection已拥有顺畅、卓越的使用体验，被工业质检、遥感图像检测、无人巡检、新零售、互联网、科研等十多个行业的开发者广泛应用。
-
-
-
-**说明：**
-- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
-- `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
-- PaddleDetection增强版`YOLOv3-ResNet50vd-DCN`在COCO数据集mAP高于原作10.6个绝对百分点，推理速度为61.3FPS，快于原作约70%
-- 图中模型均可在[模型库](#模型库)中获取
-
-
-## 文档教程
-
-### 入门教程
-
-- [安装说明](docs/tutorials/INSTALL_cn.md)
-- [快速开始](docs/tutorials/QUICK_STARTED_cn.md)
-- [如何准备数据](docs/tutorials/PrepareDataSet.md)
-- [训练/评估/预测/部署流程](docs/tutorials/DetectionPipeline.md)
-- [如何自定义数据集](docs/tutorials/Custom_DataSet.md)
-- [常见问题汇总](docs/FAQ.md)
-
-### 进阶教程
-- 参数配置
- - [配置模块设计和介绍](docs/advanced_tutorials/config_doc/CONFIG_cn.md)
- - [RCNN参数说明](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- - [YOLOv3参数说明](docs/advanced_tutorials/config_doc/yolov3_mobilenet_v1.md)
-- 迁移学习
- - [如何加载预训练](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md)
-- 模型压缩(基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
- - [压缩benchmark](slim)
- - [量化](slim/quantization), [剪枝](slim/prune), [蒸馏](slim/distillation), [搜索](slim/nas)
-- 推理部署
- - [模型导出教程](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- - [服务器端Python部署](deploy/python)
- - [服务器端C++部署](deploy/cpp)
- - [移动端部署](https://github.com/PaddlePaddle/Paddle-Lite-Demo)
- - [在线Serving部署](deploy/serving)
- - [推理Benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
-- 进阶开发
- - [新增数据预处理](docs/advanced_tutorials/READER.md)
- - [新增检测算法](docs/advanced_tutorials/MODEL_TECHNICAL.md)
-
-
-## 模型库
-
-- 通用目标检测:
- - [模型库和基线](docs/MODEL_ZOO_cn.md)
- - [移动端模型](configs/mobile/README.md)
- - [Anchor Free](configs/anchor_free/README.md)
- - [PP-YOLO模型](configs/ppyolo/README_cn.md)
- - [676类目标检测](docs/featured_model/LARGE_SCALE_DET_MODEL.md)
- - [两阶段实用模型PSS-Det](configs/rcnn_enhance/README.md)
-- 通用实例分割：
- - [SOLOv2](configs/solov2/README.md)
-- 垂类领域
- - [人脸检测](docs/featured_model/FACE_DETECTION.md)
- - [行人检测](docs/featured_model/CONTRIB_cn.md)
- - [车辆检测](docs/featured_model/CONTRIB_cn.md)
-- 比赛方案
- - [Objects365 2019 Challenge夺冠模型](docs/featured_model/champion_model/CACascadeRCNN.md)
- - [Open Images 2019-Object Detction比赛最佳单模型](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
-
-## 应用案例
-
-- [人像圣诞特效自动生成工具](application/christmas)
-
-## 第三方教程推荐
-
-- [PaddleDetection在Windows下的部署(一)](https://zhuanlan.zhihu.com/p/268657833)
-- [PaddleDetection在Windows下的部署(二)](https://zhuanlan.zhihu.com/p/280206376)
-- [Jetson Nano上部署PaddleDetection经验分享](https://zhuanlan.zhihu.com/p/319371293)
-- [安全帽检测YOLOv3模型在树莓派上的部署](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/yolov3_for_raspi.md)
-- [使用SSD-MobileNetv1完成一个项目--准备数据集到完成树莓派部署](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/ssd_mobilenet_v1_for_raspi.md)
-
-## 版本更新
-v2.0-rc版本已经在`02/2021`发布，新增动态图版本，支持RCNN, YOLOv3, PP-YOLO, SSD/SSDLite, FCOS, TTFNet, SOLOv2等系列模型，支持模型剪裁和量化，支持预测部署及TensorRT推理加速，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
-
-## 许可证书
-本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
-
-
-## 贡献代码
-
-我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。
diff --git a/static/README_en.md b/static/README_en.md
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-English | [简体中文](README_cn.md)
-
-Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
-
-# Introduction
-
-PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of constructing, training, optimizing and deploying detection models in a faster and better way.
-
-PaddleDetection implements varied mainstream object detection algorithms in modular design, and provides wealthy data augmentation methods, network components(such as backbones), loss functions, etc., and integrates abilities of model compression and cross-platform high-performance deployment.
-
-After a long time of industry practice polishing, PaddleDetection has had smooth and excellent user experience, it has been widely used by developers in more than ten industries such as industrial quality inspection, remote sensing image object detection, automatic inspection, new retail, Internet, and scientific research.
-
-
-
-
-
-### Product dynamic
-
-- 2020.11.20: Release `release/0.5` version, Please refer to [change log](docs/CHANGELOG.md) for details.
-- 2020.11.10: Added [SOLOv2](configs/solov2) as an instance segmentation model, which reached 38.6 FPS on a single Tesla V100, 38.8 mask AP on Coco-Val dataset, and inference speed increased by 24% and mAP by 2.4 percentage points.
-- 2020.10.30: PP-YOLO support rectangular image input, and add a new PACT quantization strategy for slim。
-- 2020.09.30: Released the [mobile-side detection demo](deploy/android_demo), and you can directly scan the code for installation experience.
-- 2020.09.21-27: [Object detection 7 days of punching class] Hand in hand to teach you from the beginning to the advanced level, in-depth understanding of the object detection algorithm life. Join the course QQ group (1136406895) to study together :)
-- 2020.07.24: [PP-YOLO](https://arxiv.org/abs/2007.12099), which is **the most practical** object detection model, was released, it deeply considers the double demands of industrial applications for accuracy and speed, and reached accuracy as 45.2% (the latest 45.9%) on COCO dataset, inference speed as 72.9 FPS on a single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
-- 2020.06.11: Publish 676 classes of large-scale server-side practical object detection models that are applicable to most application scenarios and can be used directly for prediction or for fine-tuning other tasks.
-
-### Features
-
-- **Rich Models**
-PaddleDetection provides rich of models, including **100+ pre-trained models** such as **object detection**, **instance segmentation**, **face detection** etc. It covers a variety of **global competition champion** schemes.
-
-- **Use Concisely**
-Modular design, decouple each network component, developers easily build and try various detection models and optimization strategies, quickly get high-performance, customized algorithm.
-
-- **Getting Through End to End**
-From data augmentation, constructing models, training, compression, depolyment, get through end to end, and complete support for multi-architecture, multi-device deployment for **cloud and edge device**.
-
-- **High Performance:**
-Based on the high performance core of PaddlePaddle, advantages of training speed and memory occupation are obvious. Support FP16 training, support multi-machine training.
-
-#### Overview of Kit Structures
-
-
-
-
-
- Architectures
-
-
- Backbones
-
-
- Components
-
-
- Data Augmentation
-
-
-
-
-
Two-Stage Detection
-
-
Faster RCNN
-
FPN
-
Cascade-RCNN
-
Libra RCNN
-
Hybrid Task RCNN
-
PSS-Det RCNN
-
-
-
One-Stage Detection
-
-
RetinaNet
-
YOLOv3
-
YOLOv4
-
PP-YOLO
-
SSD
-
-
-
Anchor Free
-
-
CornerNet-Squeeze
-
FCOS
-
TTFNet
-
-
-
-
Instance Segmentation
-
-
Mask RCNN
-
SOLOv2
-
-
-
-
Face-Detction
-
-
FaceBoxes
-
BlazeFace
-
BlazeFace-NAS
-
-
-
-
-
-
ResNet(&vd)
-
ResNeXt(&vd)
-
SENet
-
Res2Net
-
HRNet
-
Hourglass
-
CBNet
-
GCNet
-
DarkNet
-
CSPDarkNet
-
VGG
-
MobileNetv1/v3
-
GhostNet
-
Efficientnet
-
-
-
-
Common
-
-
Sync-BN
-
Group Norm
-
DCNv2
-
Non-local
-
-
-
FPN
-
-
BiFPN
-
BFP
-
HRFPN
-
ACFPN
-
-
-
Loss
-
-
Smooth-L1
-
GIoU/DIoU/CIoU
-
IoUAware
-
-
-
Post-processing
-
-
SoftNMS
-
MatrixNMS
-
-
-
Speed
-
-
FP16 training
-
Multi-machine training
-
-
-
-
-
-
Resize
-
Flipping
-
Expand
-
Crop
-
Color Distort
-
Random Erasing
-
Mixup
-
Cutmix
-
Grid Mask
-
Auto Augment
-
-
-
-
-
-
-
-
-
-
-#### Overview of Model Performance
-The relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
-
-
-
-
-
-**NOTE:**
-
-- `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
-
-- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
-
-- The enhanced PaddleDetection model `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is 61.3 fps, nearly 70% faster than the darknet framework.
-All these models can be get in [Model Zoo](#ModelZoo)
-
-
-## Tutorials
-
-### Get Started
-
-- [Installation guide](docs/tutorials/INSTALL_cn.md)
-- [Quick start on small dataset](docs/tutorials/QUICK_STARTED_cn.md)
-- [Prepare dataset](docs/tutorials/PrepareDataSet.md)
-- [Train/Evaluation/Inference/Deploy](docs/tutorials/DetectionPipeline.md)
-- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md)
-- [FAQ](docs/FAQ.md)
-
-### Advanced Tutorials
-
-- Parameter configuration
- - [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG_cn.md)
- - [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- - [Parameter configuration for YOLOv3 model](docs/advanced_tutorials/config_doc/yolov3_mobilenet_v1.md)
-
-- Tansfer learning
- - [How to load pretrained model](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md)
-
-- Model Compression(Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
- - [Model compression benchmark](slim)
- - [Quantization](slim/quantization)
- - [Model pruning](slim/prune)
- - [Model distillation](slim/distillation)
- - [Neural Architecture Search](slim/nas)
-
-- Inference and deployment
- - [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- - [Python inference](deploy/python)
- - [C++ inference](deploy/cpp)
- - [Mobile](https://github.com/PaddlePaddle/Paddle-Lite-Demo)
- - [Serving](deploy/serving)
- - [Inference benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
-
-- Advanced development
- - [New data augmentations](docs/advanced_tutorials/READER.md)
- - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md)
-
-
-## Model Zoo
-
-- Universal object detection
- - [Model library and baselines](docs/MODEL_ZOO_cn.md)
- - [Mobile models](configs/mobile/README.md)
- - [Anchor free models](configs/anchor_free/README.md)
- - [PP-YOLO](configs/ppyolo/README_cn.md)
- - [676 classes of object detection](docs/featured_model/LARGE_SCALE_DET_MODEL.md)
- - [Two-stage practical PSS-Det](configs/rcnn_enhance/README.md)
-- Universal instance segmentation
- - [SOLOv2](configs/solov2/README.md)
-- Vertical field
- - [Face detection](docs/featured_model/FACE_DETECTION.md)
- - [Pedestrian detection](docs/featured_model/CONTRIB_cn.md)
- - [Vehicle detection](docs/featured_model/CONTRIB_cn.md)
-- Competition Plan
- - [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md)
- - [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
-
-## Applications
-
-- [Christmas portrait automatic generation tool](application/christmas)
-
-## Updates
-
-v2.0-rc was released at `02/2021`, add dygraph version, which supports RCNN, YOLOv3, PP-YOLO, SSD/SSDLite, FCOS, TTFNet, SOLOv2, etc. supports model pruning and quantization, supports deploying and accelerating by TensorRT, etc. Please refer to [change log](docs/CHANGELOG.md) for details.
-
-
-## License
-
-PaddleDetection is released under the [Apache 2.0 license](LICENSE).
-
-
-## Contributing
-
-Contributions are highly welcomed and we would really appreciate your feedback!!
diff --git a/static/application/christmas/README.md b/static/application/christmas/README.md
deleted file mode 100644
index 1500544d9d521955f56fd065af6bc395d1240278..0000000000000000000000000000000000000000
--- a/static/application/christmas/README.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# 人像圣诞特效自动生成工具
-通过SOLOv2实例分割模型分割人像，并通过BlazeFace关键点模型检测人脸关键点，然后根据两个模型输出结果更换圣诞风格背景并为人脸加上圣诞老人胡子、圣诞眼镜及圣诞帽等特效。本项目通过PaddleHub可直接发布Server服务，供本地调试与前端直接调用接口。您可通过以下二维码中微信小程序直接体验：
-
-
+
产品动态
-- 🔥 **2022.3.24：PaddleDetection发布[release/2.4版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)**
+- 🔥 **2022.7.14：[行人分析工具PP-Human v2](./deploy/pipeline)发布**
+ - 四大产业特色功能：高性能易扩展的五大复杂行为识别、闪电级人体属性识别、一行代码即可实现的人流检测与轨迹留存以及高精度跨镜跟踪
+ - 底层核心算法性能强劲：覆盖行人检测、跟踪、属性三类核心算法能力，对目标人数、光线、背景均无限制
+ - 极低使用门槛：提供保姆级全流程开发及模型优化策略、一行命令完成推理、兼容各类数据输入格式
+**活动预告** 7月19日晚20点，PaddleDetection举办PP-Human v2线上私享交流会，欢迎大家扫码进群，获取线上会议链接！名额有限，抓紧报名！
+
引用
diff --git a/README_en.md b/README_en.md
index 820ed56b97a55a7b749ecd0bebf24b5cf7d4f9e5..3a67a541cbff42f27d6b47b6df37f7ff1642ec02 100644
--- a/README_en.md
+++ b/README_en.md
@@ -1,11 +1,11 @@
-English | [简体中文](README_cn.md)
+简体中文 | [English](README_en.md)
@@ -13,70 +13,71 @@ English | [简体中文](README_cn.md)
- - Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), supporting s/m/l/x version, achieving mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, supporting AMP training and its training speed is 33% faster than PP-YOLOv2.
- - Release enhanced models of [PP-PicoDet](configs/picodet), including PP-PicoDet-XS model with 0.7M parameters, its mAP promoted ~2% on COCO, inference speed accelerated 63% on CPU, and post-processing integrated into the network to optimize deployment pipeline.
- - Release real-time human analysis tool [PP-Human](deploy/pipeline), which is based on data from real-life situations, supporting pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics and action recognition.
- - Release [YOLOX](configs/yolox), supporting nano/tiny/s/m/l/x version, achieving mAP as 51.8% on COCO val dataset by YOLOX-x.
+
Introduction
+- 2021.11.03: PaddleDetection released [release/2.3 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3)
-PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular design with configurable modules such as network components, data augmentations and losses. It releases many kinds SOTA industry practice models and integrates abilities of model compression and cross-platform high-performance deployment to help developers in the whole process with a faster and better way.
+ - Release light-weight featured detection model ⚡[PP-PicoDet](configs/picodet). With a 0.99m parameter, its inference speed could reach to 150FPS when COCO mAP as over 30%
+ - Release light-weight keypoint special model ⚡[PP-TinyPose](configs/keypoint/tiny_pose), FP16 inference speed as 122 FPS and on a single person detection. It has high performance and fast speed, unlimited detection headcounts while being effective on small objects.
+ - Release real-time tracking system [PP-Tracking](deploy/pptracking), covering pedestrian, vehicle and multi-category tracking with single and multi-camera, optimization for small and intensive objects, providing technical solutions for human and vehicle traffic.
+ - Add object detection models [Swin Transformer](configs/faster_rcnn)，[TOOD](configs/tood)，[GFL](configs/gfl)
+ - Release optimized small object detection model [Sniper](configs/sniper) and [PP-YOLO-EB](configs/ppyolo) model which optimized for EdgeBoard
+ - Add light-weight keypoint model [Lite HRNet](configs/keypoint) and supported Paddle Lite deployment
-#### PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc.
+- [More releases](https://github.com/PaddlePaddle/PaddleDetection/releases)
-
Features
-
-- **Rich Models**
-
- PaddleDetection provides rich of models, including **250+ pre-trained models** such as **object detection**, **instance segmentation**, **face detection**, **keypoint detection**, **multi-object tracking** and etc, covering a variety of **global competition champion** schemes.
-- **Highly Flexible**
- Components are designed to be modular. Model architectures, as well as data preprocess pipelines and optimization strategies, can be easily customized with simple configuration changes.
-
-- **Production Ready**
-
- From data augmentation, constructing models, training, compression, depolyment, get through end to end, and complete support for multi-architecture, multi-device deployment for **cloud and edge device**.
+## 
Exchanges
-- If you have any problem or suggestion on PaddleDetection, please send us issues through [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues).
+- If you have any question or suggestion, please give us your valuable input via [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues)
-- Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").
+ Welcome to join PaddleDetection user groups on QQ, WeChat (scan the QR code, add and reply "D" to the assistant)
Overview of Kit Structures
+## 
Overview of Model Performance
+## 
Tutorials
-
-### Get Started
-
-- [Installation Guide](docs/tutorials/INSTALL.md)
-- [Prepare Dataset](docs/tutorials/PrepareDataSet_en.md)
-- [Quick Start on PaddleDetection](docs/tutorials/GETTING_STARTED.md)
-
-### Advanced Tutorials
-
-- Parameter Configuration
-
- - [Parameter configuration for RCNN model](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md)
- - [Parameter configuration for PP-YOLO model](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation_en.md)
-
-- Model Compression(Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
-
- - [Prune/Quant/Distill](configs/slim)
-
-- [Metric Logging during Model Training](docs/tutorials/logging_en.md)
-
-- Inference and Deployment
-
- - [Export model for inference](deploy/EXPORT_MODEL_en.md)
- - [Paddle Inference](deploy/README_en.md)
- - [Python inference](deploy/python)
- - [C++ inference](deploy/cpp)
- - [Paddle-Lite](deploy/lite)
- - [Paddle Serving](deploy/serving)
- - [Export ONNX model](deploy/EXPORT_ONNX_MODEL_en.md)
- - [Inference benchmark](deploy/BENCHMARK_INFER_en.md)
- - [Exporting to ONNX and using OpenVINO for inference](docs/advanced_tutorials/openvino_inference/README.md)
-
-- Advanced Development
-
- - [New data augmentations](docs/advanced_tutorials/READER_en.md)
- - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md)
-
-## 
Model Zoo
-
-- General Object Detection
- - [Model library and baselines](docs/MODEL_ZOO_cn.md)
- - [PP-YOLOE](configs/ppyoloe/README_cn.md)
- - [PP-YOLO](configs/ppyolo/README.md)
- - [PP-PicoDet](configs/picodet/README.md)
- - [Enhanced Anchor Free model--TTFNet](configs/ttfnet/README_en.md)
- - [Mobile models](static/configs/mobile/README_en.md)
- - [676 classes of object detection](static/docs/featured_model/LARGE_SCALE_DET_MODEL_en.md)
- - [Two-stage practical PSS-Det](configs/rcnn_enhance/README_en.md)
- - [SSLD pretrained models](docs/feature_models/SSLD_PRETRAINED_MODEL_en.md)
-- General Instance Segmentation
- - [SOLOv2](configs/solov2/README.md)
-- Rotated Object Detection
- - [S2ANet](configs/dota/README_en.md)
-- [Keypoint Detection](configs/keypoint)
- - [PP-TinyPose](configs/keypoint/tiny_pose)
- - HigherHRNet
- - HRNet
- - LiteHRNet
-- [Multi-Object Tracking](configs/mot/README.md)
- - [PP-Tracking](deploy/pptracking/README_en.md)
- - [DeepSORT](configs/mot/deepsort/README.md)
- - [JDE](configs/mot/jde/README.md)
- - [FairMOT](configs/mot/fairmot/README.md)
- - [ByteTrack](configs/mot/bytetrack/README.md)
-- Practical Specific Models
- - [Face detection](configs/face_detection/README_en.md)
- - [Pedestrian detection](configs/pedestrian/README.md)
- - [Vehicle detection](configs/vehicle/README.md)
-- Scienario Solution
- - [Real-Time Human Analysis Tool PP-Human](deploy/pipeline)
-- Competition Solution
- - [Objects365 2019 Challenge champion model](static/docs/featured_model/champion_model/CACascadeRCNN_en.md)
- - [Best single model of Open Images 2019-Object Detection](static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL_en.md)
+**Clarification：**
+
+- Tests were conducted on Qualcomm Snapdragon 865 (4 \*A77 + 4 \*A55) batch_size=1, 4 thread, and NCNN inference library, test script see [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
+- [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are self-developed models of PaddleDetection, and other models are not tested yet.
+
+
Applications
-- [Christmas portrait automatic generation tool](static/application/christmas)
-- [Android Fitness Demo](https://github.com/zhiboniu/pose_demo_android)
+- [Fitness app on android mobile](https://github.com/zhiboniu/pose_demo_android)
+- [PP-Tracking GUI Visualization Interface](https://github.com/yangyudong2020/PP-Tracking_GUi)
+
+## Recommended third-party tutorials
+
+- [Deployment of PaddleDetection for Windows I ](https://zhuanlan.zhihu.com/p/268657833)
+- [Deployment of PaddleDetection for Windows II](https://zhuanlan.zhihu.com/p/280206376)
+- [Deployment of PaddleDetection on Jestson Nano](https://zhuanlan.zhihu.com/p/319371293)
+- [How to deploy YOLOv3 model on Raspberry Pi for Helmet detection](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/yolov3_for_raspi.md)
+- [Use SSD-MobileNetv1 for a project -- From dataset to deployment on Raspberry Pi](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/ssd_mobilenet_v1_for_raspi.md)
-## 
Updates
+## 
License
+## 
Contribution
+## 
|
| **属性分析** | 兼容多种数据格式：支持图片、视频输入
|
| **行为识别** | 功能丰富：支持摔倒、打架、抽烟、打电话、人员闯入五种高频异常行为识别
|
-| **人流量计数与轨迹记录** | 简洁易用：单个参数即可开启人流量计数与轨迹记录功能 | 
|
+| **人流量计数**
+ 
+
+ 
下面以PaddleDetection目前已经支持的几个具体动作为例，介绍每个动作方案的选型依据：
### 吸烟
-方案选择：基于人体id的检测
+方案选择：基于人体id检测的行为识别
原因：吸烟动作中具有香烟这个明显特征目标，因此我们可以认为当在某个人物的对应图像中检测到香烟时，该人物即在吸烟动作中。相比于基于视频或基于骨骼点的识别方案，训练检测模型需要采集的是图片级别而非视频级别的数据，可以明显减轻数据收集与标注的难度。此外，目标检测任务具有丰富的预训练模型资源，整体模型的效果会更有保障，
### 打电话
-方案选择：基于人体id的分类
+方案选择：基于人体id分类的行为识别
原因：打电话动作中虽然有手机这个特征目标，但为了区分看手机等动作，以及考虑到在安防场景下打电话动作中会出现较多对手机的遮挡（如手对手机的遮挡、人头对手机的遮挡等等），不利于检测模型正确检测到目标。同时打电话通常持续的时间较长，且人物本身的动作不会发生太大变化，因此可以因此采用帧级别图像分类的策略。
此外，打电话这个动作主要可以通过上半身判别，可以采用半身图片，去除冗余信息以降低模型训练的难度。
@@ -36,6 +30,12 @@
原因：摔倒是一个明显的时序行为的动作，可由一个人物本身进行区分，具有场景无关的特性。由于PP-Human的场景定位偏向安防监控场景，背景变化较为复杂，且部署上需要考虑到实时性，因此采用了基于骨骼点的行为识别方案，以获得更好的泛化性及运行速度。
+### 闯入
+
+方案选择：基于人体id跟踪的行为识别
+
+原因：闯入识别判断行人的路径或所在位置是否在某区域内即可，与人体自身动作无关，因此只需要跟踪人体跟踪结果分析是否存在闯入行为。
+
### 打架
方案选择：基于视频分类的行为识别
@@ -45,7 +45,8 @@
下面详细展开四大类方案的数据准备、模型优化和新增行为识别方法
-1. [基于人体id的检测](./idbased_det.md)
-2. [基于人体id的分类](./idbased_clas.md)
+1. [基于人体id检测的行为识别](./idbased_det.md)
+2. [基于人体id分类的行为识别](./idbased_clas.md)
3. [基于人体骨骼点的行为识别](./skeletonbased_rec.md)
-4. [基于视频分类的行为识别](./videobased_rec.md)
+4. [基于人体id跟踪的行为识别](../mot.md)
+5. [基于视频分类的行为识别](./videobased_rec.md)
diff --git a/docs/advanced_tutorials/openvino_inference/README.md b/docs/advanced_tutorials/openvino_inference/README.md
index 0a67651d9baadf6708adcda21628f9a1dcaaf31c..ac372eaa30d1f02a5016b42b928e9c56bfea547a 100644
--- a/docs/advanced_tutorials/openvino_inference/README.md
+++ b/docs/advanced_tutorials/openvino_inference/README.md
@@ -3,9 +3,9 @@
## Introduction
PaddleDetection has been a vibrant open-source project and has a large amout of contributors and maintainers around it. It is an AI framework which enables developers to quickly integrate AI capacities into their own projects and applications.
-Intel OpenVINO is a widely used free toolkit. It facilitates the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware.
+Intel OpenVINO is a widely used free toolkit. It facilitates the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware.
-Apparently, the upstream(Paddle) and the downstream(Intel OpenVINO) can work together to streamline and simplify the process of developing an AI model and deploying the model onto hardware, which, in turn, makes our lives easier.
+Apparently, the upstream(Paddle) and the downstream(Intel OpenVINO) can work together to streamline and simplify the process of developing an AI model and deploying the model onto hardware, which, in turn, makes our lives easier.
This article will show you how to use a PaddleDetection model [FairMOT](../../../configs/mot/fairmot/README.md) from the Model Zoo in PaddleDetection and use it with OpenVINO to do the inference.
@@ -50,7 +50,7 @@ Once the Paddle model has been converted to ONNX format, we can then use it with
1. ### Get the execution network
-So the 1st thing to do here is to get an execution network which can be used later to do the inference.
+So the 1st thing to do here is to get an execution network which can be used later to do the inference.
Here is the code.
@@ -70,7 +70,7 @@ Every AI model has its own steps of preprocessing, let's have a look how to do i
```
def prepare_input():
transforms = [
- T.Resize(target_size=(target_width, target_height)),
+ T.Resize(target_size=(target_width, target_height)),
T.Normalize(mean=(0,0,0), std=(1,1,1))
]
img_file = root_path / "images/street.jpeg"
@@ -87,7 +87,7 @@ def prepare_input():
3. ### Prediction
-After we have done all the load network and preprocessing, it finally comes to the stage of prediction.
+After we have done all the load network and preprocessing, it finally comes to the stage of prediction.
```
@@ -100,7 +100,7 @@ You might be surprised to see the very exciting stage this small. Hang on there,
4. ### Post-processing
-MOT(Multi-Object Tracking) is special, not like other AI models which require a few steps of post-processing. Instead, FairMOT requires a special object called tracker, to handle the prediction results. The prediction results are prediction detections and prediction embeddings.
+MOT(Multi-Object Tracking) is special, not like other AI models which require a few steps of post-processing. Instead, FairMOT requires a special object called tracker, to handle the prediction results. The prediction results are prediction detections and prediction embeddings.
Luckily, PaddleDetection has made this procesure easy for us, it has exported the JDETracker from `ppdet`, so that we do not need to write much code to handle it.
@@ -156,4 +156,4 @@ So these are the all steps which you need to follow in order to run FairMOT on y
A companion article which explains in details of this procedure will be released soon and a link to that article will be updated here soon.
-To see the full code, please take a look at [Paddle OpenVINO Prediction](docs/advanced_tutorials/openvino_inference/fairmot_onnx_openvino.py).
\ No newline at end of file
+To see the full code, please take a look at [Paddle OpenVINO Prediction](./fairmot_onnx_openvino.py).
diff --git a/docs/advanced_tutorials/openvino_inference/README_cn.md b/docs/advanced_tutorials/openvino_inference/README_cn.md
index 2fc001757157501bccaeffc37d900cbc6d31e7eb..aaaf84eb05c26359fcc48cb14a3f6104bd834d5d 100644
--- a/docs/advanced_tutorials/openvino_inference/README_cn.md
+++ b/docs/advanced_tutorials/openvino_inference/README_cn.md
@@ -68,7 +68,7 @@ def get_net():
```
def prepare_input():
transforms = [
- T.Resize(target_size=(target_width, target_height)),
+ T.Resize(target_size=(target_width, target_height)),
T.Normalize(mean=(0,0,0), std=(1,1,1))
]
img_file = root_path / "images/street.jpeg"
@@ -93,7 +93,7 @@ def predict(exec_net, input):
return result
```
-您可能会惊讶地看到, 最激动人心的步骤居然如此简单。 不过下一个阶段会加复杂。
+您可能会惊讶地看到, 最激动人心的步骤居然如此简单。 不过下一个阶段会更加复杂。
4. ### 后处理
@@ -138,7 +138,7 @@ def postprocess(pred_dets, pred_embs, threshold = 0.5):
5. ### 画出检测框(可选)
-这一步是可选的。出于演示目的，我只使用 `plot_tracking_dict()` 方法在图像上绘制所有边界框。 但是，如果您没有相同的要求，则不需要这样做。
+这一步是可选的。出于演示目的，我只使用 `plot_tracking_dict()` 方法在图像上绘制所有边界框。 但是，如果您没有相同的要求，则不需要这样做。
```
online_im = plot_tracking_dict(
@@ -154,4 +154,4 @@ online_im = plot_tracking_dict(
之后会有一篇详细解释此过程的配套文章将会发布，并且该文章的链接将很快在此处更新。
-完整代码请查看 [Paddle OpenVINO 预测](docs/advanced_tutorials/openvino_inference/fairmot_onnx_openvino.py).
\ No newline at end of file
+完整代码请查看 [Paddle OpenVINO 预测](./fairmot_onnx_openvino.py).
diff --git a/static/LICENSE b/static/LICENSE
deleted file mode 100644
index 261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64..0000000000000000000000000000000000000000
--- a/static/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/static/README.md b/static/README.md
deleted file mode 120000
index 4015683cfa5969297febc12e7ca1264afabbc0b5..0000000000000000000000000000000000000000
--- a/static/README.md
+++ /dev/null
@@ -1 +0,0 @@
-README_cn.md
\ No newline at end of file
diff --git a/static/README_cn.md b/static/README_cn.md
deleted file mode 100644
index 9a9696ea2ee2b79e67cf681ec786adaf563a4012..0000000000000000000000000000000000000000
--- a/static/README_cn.md
+++ /dev/null
@@ -1,261 +0,0 @@
-简体中文 | [English](README_en.md)
-
-文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
-
-# 简介
-
-PaddleDetection飞桨目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。
-
-PaddleDetection模块化地实现了多种主流目标检测算法，提供了丰富的数据增强策略、网络模块组件（如骨干网络）、损失函数等，并集成了模型压缩和跨平台高性能部署能力。
-
-经过长时间产业实践打磨，PaddleDetection已拥有顺畅、卓越的使用体验，被工业质检、遥感图像检测、无人巡检、新零售、互联网、科研等十多个行业的开发者广泛应用。
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