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Commit fccfdfa5 authored by dlyrm's avatar dlyrm
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update code

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# Virtualenv
/.venv/
/venv/
# Byte-compiled / optimized / DLL files
__pycache__/
.ipynb_checkpoints/
*.py[cod]
# C extensions
*.so
# json file
*.json
# log file
*.log
# Distribution / packaging
/bin/
/build/
/develop-eggs/
/dist/
/eggs/
/lib/
/lib64/
/output/
/inference_model/
/output_inference/
/parts/
/sdist/
/var/
/*.egg-info/
/.installed.cfg
/*.egg
/.eggs
# AUTHORS and ChangeLog will be generated while packaging
/AUTHORS
/ChangeLog
# BCloud / BuildSubmitter
/build_submitter.*
/logger_client_log
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
.tox/
.coverage
.cache
.pytest_cache
nosetests.xml
coverage.xml
# Translations
*.mo
# Sphinx documentation
/docs/_build/
*.tar
*.pyc
.idea/
dataset/coco/annotations
dataset/coco/train2017
dataset/coco/val2017
dataset/voc/VOCdevkit
dataset/fruit/fruit-detection/
dataset/voc/test.txt
dataset/voc/trainval.txt
dataset/wider_face/WIDER_test
dataset/wider_face/WIDER_train
dataset/wider_face/WIDER_val
dataset/wider_face/wider_face_split
ppdet/version.py
# NPU meta folder
kernel_meta/
# MAC
*.DS_Store
.pre-commit-config.yaml 0 → 100644
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- repo: https://github.com/PaddlePaddle/mirrors-yapf.git
sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
hooks:
- id: yapf
files: \.py$
- repo: https://github.com/pre-commit/pre-commit-hooks
sha: a11d9314b22d8f8c7556443875b731ef05965464
hooks:
- id: check-merge-conflict
- id: check-symlinks
- id: detect-private-key
files: (?!.*paddle)^.*$
- id: end-of-file-fixer
files: \.(md|yml)$
- id: trailing-whitespace
files: \.(md|yml)$
- repo: https://github.com/Lucas-C/pre-commit-hooks
sha: v1.0.1
hooks:
- id: forbid-crlf
files: \.(md|yml)$
- id: remove-crlf
files: \.(md|yml)$
- id: forbid-tabs
files: \.(md|yml)$
- id: remove-tabs
files: \.(md|yml)$
- repo: local
hooks:
- id: clang-format-with-version-check
name: clang-format
description: Format files with ClangFormat.
entry: bash ./.travis/codestyle/clang_format.hook -i
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
- repo: local
hooks:
- id: cpplint-cpp-source
name: cpplint
description: Check C++ code style using cpplint.py.
entry: bash ./.travis/codestyle/cpplint_pre_commit.hook
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx)$
.style.yapf 0 → 100644
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[style]
based_on_style = pep8
column_limit = 80
.travis.yml 0 → 100644
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language: cpp
cache: ccache
sudo: required
dist: trusty
services:
- docker
os:
- linux
env:
- JOB=PRE_COMMIT
addons:
apt:
packages:
- git
- python
- python-pip
- python2.7-dev
ssh_known_hosts: 13.229.163.131
before_install:
- sudo pip install -U virtualenv pre-commit pip -i https://pypi.tuna.tsinghua.edu.cn/simple
- docker pull paddlepaddle/paddle:latest
- git pull https://github.com/PaddlePaddle/PaddleDetection develop
script:
- exit_code=0
- .travis/precommit.sh || exit_code=$(( exit_code | $? ))
# - docker run -i --rm -v "$PWD:/py_unittest" paddlepaddle/paddle:latest /bin/bash -c
# 'cd /py_unittest; sh .travis/unittest.sh' || exit_code=$(( exit_code | $? ))
- if [ $exit_code -eq 0 ]; then true; else exit 1; fi;
notifications:
email:
on_success: change
on_failure: always
LICENSE 0 → 100644
View file @ fccfdfa5
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README.md
View file @ fccfdfa5
# Yolov5_paddle
# yolov5_paddle
## 论文
无论文
## 模型结构
YOLOv5 是一种目标检测算法，采用单阶段（one-stage）的方法，基于轻量级的卷积神经网络结构，通过引入不同尺度的特征融合和特征金字塔结构来实现高效准确的目标检测。
![Backbone.png](asserts%2FBackbone.png)
## 算法原理
YOLOv5 是一种基于单阶段目标检测算法，通过将图像划分为不同大小的网格，预测每个网格中的目标类别和边界框，利用特征金字塔结构和自适应的模型缩放来实现高效准确的实时目标检测。
![Algorithm_principle.png](asserts%2FAlgorithm_principle.png)
## 环境配置
### Docker (方法一)
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/paddlepaddle:2.4.2-centos7.6-dtk-23.04-py38-latest
docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /path/workspace/
pip3 install -r requirements.txt
```
### Dockerfile (方法二)
```
cd ./docker
docker build --no-cache -t yolov5_paddle:last .
docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
```
### Anaconda (方法三)
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
DTK软件栈：dtk23.04
python：python3.8
paddle：2.4.2
```
Tips：以上dtk软件栈、python、paddle等DCU相关工具版本需要严格一一对应
2、其他非特殊库直接按照requirements.txt安装
```
pip3 install -r requirements.txt
```
## 数据集
COCO2017（在网络良好的情况下，如果没有下载数据集，程序会默认在线下载数据集）
[训练数据](http://images.cocodataset.org/zips/train2017.zip)
[验证数据](http://images.cocodataset.org/zips/val2017.zip)
[测试数据](http://images.cocodataset.org/zips/test2017.zip)
[标签数据](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip)
数据集的目录结构如下：
```
├── images
│ ├── train2017
│ ├── val2017
│ ├── test2017
├── labels
│ ├── train2017
│ ├── val2017
├── annotations
│ ├── instances_val2017.json
├── LICENSE
├── README.txt
├── test-dev2017.txt
├── train2017.txt
├── val2017.txt
```
## 训练
### 单机单卡
```
export HIP_VISIBLE_DEVICES=0
export USE_MIOPEN_BATCHNORM=1
python3 train.py --batch 32 --data coco.yaml --cfg 'yolov5m.yaml' --weights '' --project 'run/train' --hyp 'data/hyps/hyp.scratch-high.yaml' --epochs 1000 2>&1 | tee yolov5m.log
```
### 单机多卡
```
#以单机四卡为例子
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export HSA_FORCE_FINE_GRAIN_PCIE=1
export USE_MIOPEN_BATCHNORM=1
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/yolov5/yolov5_s_300e_coco.yml --amp --eval
```
## 推理
#### 单卡推理
```
HIP_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
```
## result
此处以yolov5s模型进行推理测试
![result_000000014439_640x640.jpg](asserts%2Fresult_000000014439_640x640.jpg)
## 精度
| 模型 | 数据类型 | map0.5:0.95 | map0.5 |
|:-------:|:----:|:-----------:|:------:|
| yolov5s | 单精 | 37.0 | 56.4 |
| yolov5s | 混精 | 37.2 | 56.4 |
## 应用场景
### 算法分类
目标检测
### 热点应用行业
金融,交通,教育
## 源码仓库及问题反馈
https://developer.hpccube.com/codes/modelzoo/yolov5_pytorch
## 参考
[GitHub - ultralytics/yolov5 at v6.0](https://github.com/ultralytics/yolov5/tree/v6.0)
README_cn.md 0 → 100644
View file @ fccfdfa5
简体中文 | [English](README_en.md)
## 简介
**PaddleYOLO**是基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的YOLO系列模型库，**只包含YOLO系列模型的相关代码**，支持`YOLOv3`、`PP-YOLO`、`PP-YOLOv2`、`PP-YOLOE`、**`PP-YOLOE+`**、**`RT-DETR`**、`YOLOX`、`YOLOv5`、`YOLOv6`、`YOLOv7`、`YOLOv8`、`YOLOv5u`、`YOLOv7u`、`YOLOv6Lite`、`RTMDet`等模型，COCO数据集模型库请参照 [ModelZoo](docs/MODEL_ZOO_cn.md) 和 [configs](configs/)。
<div align="center">
<img src="https://user-images.githubusercontent.com/13104100/213197403-c8257486-9ac4-486f-a0d5-4e3fe27ca852.jpg" width="480"/>
<img src="https://user-images.githubusercontent.com/13104100/213197635-eeb55433-bb2d-44f6-b374-73c616cfab24.jpg" width="480"/>
</div>
**注意:**
- **PaddleYOLO** 代码库协议为 **[GPL 3.0](LICENSE)**，[YOLOv5](configs/yolov5)、[YOLOv6](configs/yolov6)、[YOLOv7](configs/yolov7)和[YOLOv8](configs/yolov8)这几类模型代码不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)，其余YOLO模型推荐在[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)中使用，**会最先发布PP-YOLO系列特色检测模型的最新进展**；
- **PaddleYOLO**代码库**推荐使用paddlepaddle-2.4.2以上的版本**，请参考[官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载对应适合版本，**Windows平台请安装paddle develop版本**；
- **PaddleYOLO 的[Roadmap](https://github.com/PaddlePaddle/PaddleYOLO/issues/44)** issue用于收集用户的需求，欢迎提出您的建议和需求；
## 教程
<details open>
<summary>安装</summary>
Clone 代码库和安装 [requirements.txt](./requirements.txt)，环境需要在一个
[**Python>=3.7.0**](https://www.python.org/) 下的环境，且需要安装
[**PaddlePaddle>=2.4.2**](https://www.paddlepaddle.org.cn/install/)。
```bash
git clone https://github.com/PaddlePaddle/PaddleYOLO # clone
cd PaddleYOLO
pip install -r requirements.txt # install
```
</details>
<details open>
<summary>训练/验证/预测/</summary>
将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行。
```bash
model_name=ppyoloe # 可修改，如 yolov7
job_name=ppyoloe_plus_crn_s_80e_coco # 可修改，如 yolov7_tiny_300e_coco
config=configs/${model_name}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 1.训练（单卡/多卡），加 --eval 表示边训边评估，加 --amp 表示混合精度训练
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amp
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.评估，加 --classwise 表示输出每一类mAP
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.预测 (单张图/图片文件夹）
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5
# CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_dir=demo/ --draw_threshold=0.5
```
</details>
<details>
<summary>部署/测速</summary>
将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行。
```bash
model_name=ppyoloe # 可修改，如 yolov7
job_name=ppyoloe_plus_crn_s_80e_coco # 可修改，如 yolov7_tiny_300e_coco
config=configs/${model_name}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 4.导出模型，以下3种模式选一种
## 普通导出，加trt表示用于trt加速，对NMS和silu激活函数提速明显
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # trt=True
## exclude_post_process去除后处理导出，返回和YOLOv5导出ONNX时相同格式的concat后的1个Tensor，是未缩放回原图的坐标+分类置信度
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_post_process=True # trt=True
## exclude_nms去除NMS导出，返回2个Tensor，是缩放回原图后的坐标和分类置信度
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_nms=True # trt=True
# 5.部署预测，注意不能使用 去除后处理 或 去除NMS 导出后的模型去预测
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.部署测速，加 “--run_mode=trt_fp16” 表示在TensorRT FP16模式下测速，注意如需用到 trt_fp16 则必须为加 trt=True 导出的模型
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
# 7.onnx导出，一般结合 exclude_post_process去除后处理导出的模型
paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx
# 8.onnx trt测速
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp32
```
- 如果想切换模型，只要修改开头两行即可，如:
```
model_name=yolov7
job_name=yolov7_tiny_300e_coco
```
- 导出**onnx**，首先安装[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)，`pip install paddle2onnx`；
- **统计FLOPs(G)和Params(M)**，首先安装[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)，`pip install paddleslim`，然后设置[runtime.yml](configs/runtime.yml)里`print_flops: True`和`print_params: True`，并且注意确保是**单尺度**下如640x640，**打印的是MACs，FLOPs=2*MACs**。
</details>
<details open>
<summary> [训练自定义数据集](https://github.com/PaddlePaddle/PaddleYOLO/issues/43) </summary>
- 请参照[文档](docs/MODEL_ZOO_cn.md#自定义数据集)和[issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43)；
- PaddleDetection团队提供了**基于PP-YOLOE的各种垂类检测模型**的配置文件和权重，用户也可以作为参考去使用自定义数据集。请参考 [PP-YOLOE application](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppyoloe/application)、[pphuman](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/pphuman)、[ppvehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppvehicle)、[visdrone](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/visdrone) 和 [smalldet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/smalldet)。
- PaddleDetection团队也提供了**VOC数据集的各种YOLO模型**的配置文件和权重，用户也可以作为参考去使用自定义数据集。请参考 [voc](configs/voc)。
- 训练自定义数据集之前请先**确保加载了对应COCO权重作为预训练**，将配置文件中的`pretrain_weights: `设置为对应COCO模型训好的权重，一般会提示head分类层卷积的通道数没对应上，属于正常现象，是由于自定义数据集一般和COCO数据集种类数不一致；
- YOLO检测模型建议**总`batch_size`至少大于`64`**去训练，如果资源不够请**换小模型**或**减小模型的输入尺度**，为了保障较高检测精度，**尽量不要尝试单卡训和总`batch_size`小于`64`训**；
</details>
## 更新日志
* 【2023/05/21】支持[RT-DETR](configs/rtdetr)、[YOLOv8](configs/yolov8)、[YOLOv5u](configs/yolov5/yolov5u)和[YOLOv7u](configs/yolov7/yolov7u)训练全流程，支持[YOLOv6Lite](configs/yolov6/yolov6lite)预测和部署；
* 【2023/03/13】支持[YOLOv5u](configs/yolov5/yolov5u)和[YOLOv7u](configs/yolov7/yolov7u)预测和部署；
* 【2023/01/10】支持[YOLOv8](configs/yolov8)预测和部署；
* 【2022/09/29】支持[RTMDet](configs/rtmdet)预测和部署；
* 【2022/09/26】发布[PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO)模型套件，请参照[ModelZoo](docs/MODEL_ZOO_cn.md)；
* 【2022/09/19】支持[YOLOv6](configs/yolov6)新版，包括n/t/s/m/l模型；
* 【2022/08/23】发布`YOLOSeries`代码库: 支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`等YOLO模型，支持`ConvNeXt`骨干网络高精度版`PP-YOLOE`,`YOLOX`和`YOLOv5`等模型，支持PaddleSlim无损加速量化训练`PP-YOLOE`,`YOLOv5`,`YOLOv6`和`YOLOv7`等模型，详情可阅读[此文章](https://mp.weixin.qq.com/s/Hki01Zs2lQgvLSLWS0btrA)；
## <img src="https://user-images.githubusercontent.com/48054808/157793354-6e7f381a-0aa6-4bb7-845c-9acf2ecc05c3.png" width="20"/> 产品动态
- 🔥 **2023.3.14：PaddleYOLO发布[release/2.6版本](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6)**
- 💡 模型套件：
- 支持`YOLOv8`,`YOLOv5u`,`YOLOv7u`等YOLO模型预测和部署；
- 支持`Swin-Transformer`、`ViT`、`FocalNet`骨干网络高精度版`PP-YOLOE+`等模型；
- 支持`YOLOv8`在[FastDeploy](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/detection/paddledetection)中多硬件快速部署；
- 🔥 **2022.9.26：PaddleYOLO发布[release/2.5版本](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5)**
- 💡 模型套件：
- 发布[PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO)模型套件: 支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`等YOLO模型，支持`ConvNeXt`骨干网络高精度版`PP-YOLOE`,`YOLOX`和`YOLOv5`等模型，支持PaddleSlim无损加速量化训练`PP-YOLOE`,`YOLOv5`,`YOLOv6`和`YOLOv7`等模型；
- 🔥 **2022.8.26：PaddleDetection发布[release/2.5版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5)**
- 🗳 特色模型：
- 发布[PP-YOLOE+](configs/ppyoloe)，最高精度提升2.4% mAP，达到54.9% mAP，模型训练收敛速度提升3.75倍，端到端预测速度最高提升2.3倍；多个下游任务泛化性提升
- 发布[PicoDet-NPU](configs/picodet)模型，支持模型全量化部署；新增[PicoDet](configs/picodet)版面分析模型
- 发布[PP-TinyPose升级版](./configs/keypoint/tiny_pose/)增强版，在健身、舞蹈等场景精度提升9.1% AP，支持侧身、卧躺、跳跃、高抬腿等非常规动作
- 🔮 场景能力：
- 发布行人分析工具[PP-Human v2](./deploy/pipeline)，新增打架、打电话、抽烟、闯入四大行为识别，底层算法性能升级，覆盖行人检测、跟踪、属性三类核心算法能力，提供保姆级全流程开发及模型优化策略，支持在线视频流输入
- 首次发布[PP-Vehicle](./deploy/pipeline)，提供车牌识别、车辆属性分析（颜色、车型）、车流量统计以及违章检测四大功能，兼容图片、在线视频流、视频输入，提供完善的二次开发文档教程
- 💡 前沿算法：
- 全面覆盖的[YOLO家族](https://github.com/PaddlePaddle/PaddleYOLO)经典与最新模型: 包括YOLOv3，百度飞桨自研的实时高精度目标检测检测模型PP-YOLOE，以及前沿检测算法YOLOv4、YOLOv5、YOLOX，YOLOv6及YOLOv7
- 新增基于[ViT](configs/vitdet)骨干网络高精度检测模型，COCO数据集精度达到55.7% mAP；新增[OC-SORT](configs/mot/ocsort)多目标跟踪模型；新增[ConvNeXt](configs/convnext)骨干网络
- 📋 产业范例：新增[智能健身](https://aistudio.baidu.com/aistudio/projectdetail/4385813)、[打架识别](https://aistudio.baidu.com/aistudio/projectdetail/4086987?channelType=0&channel=0)、[来客分析](https://aistudio.baidu.com/aistudio/projectdetail/4230123?channelType=0&channel=0)、车辆结构化范例
- 2022.3.24：PaddleDetection发布[release/2.4版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
- 发布高精度云边一体SOTA目标检测模型[PP-YOLOE](configs/ppyoloe)，提供s/m/l/x版本，l版本COCO test2017数据集精度51.6%，V100预测速度78.1 FPS，支持混合精度训练，训练较PP-YOLOv2加速33%，全系列多尺度模型，满足不同硬件算力需求，可适配服务器、边缘端GPU及其他服务器端AI加速卡。
- 发布边缘端和CPU端超轻量SOTA目标检测模型[PP-PicoDet增强版](configs/picodet)，精度提升2%左右，CPU预测速度提升63%，新增参数量0.7M的PicoDet-XS模型，提供模型稀疏化和量化功能，便于模型加速，各类硬件无需单独开发后处理模块，降低部署门槛。
- 发布实时行人分析工具[PP-Human](deploy/pipeline)，支持行人跟踪、人流量统计、人体属性识别与摔倒检测四大能力，基于真实场景数据特殊优化，精准识别各类摔倒姿势，适应不同环境背景、光线及摄像角度。
- 新增[YOLOX](configs/yolox)目标检测模型，支持nano/tiny/s/m/l/x版本，x版本COCO val2017数据集精度51.8%。
- [更多版本发布](https://github.com/PaddlePaddle/PaddleDetection/releases)
## <img title="" src="https://user-images.githubusercontent.com/48054808/157795569-9fc77c85-732f-4870-9be0-99a7fe2cff27.png" alt="" width="20"> 简介
**PaddleDetection**为基于飞桨PaddlePaddle的端到端目标检测套件，内置**30+模型算法**及**250+预训练模型**，覆盖**目标检测、实例分割、跟踪、关键点检测**等方向，其中包括**服务器端和移动端高精度、轻量级**产业级SOTA模型、冠军方案和学术前沿算法，并提供配置化的网络模块组件、十余种数据增强策略和损失函数等高阶优化支持和多种部署方案，在打通数据处理、模型开发、训练、压缩、部署全流程的基础上，提供丰富的案例及教程，加速算法产业落地应用。
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/189026616-75f9c06c-b403-4a61-9372-0fcbed6e0662.gif" width="800"/>
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157799599-e6a66855-bac6-4e75-b9c0-96e13cb9612f.png" width="20"/> 特性
- **模型丰富**: 包含**目标检测**、**实例分割**、**人脸检测**、****关键点检测****、**多目标跟踪**等**250+个预训练模型**，涵盖多种**全球竞赛冠军**方案。
- **使用简洁**：模块化设计，解耦各个网络组件，开发者轻松搭建、试用各种检测模型及优化策略，快速得到高性能、定制化的算法。
- **端到端打通**: 从数据增强、组网、训练、压缩、部署端到端打通，并完备支持**云端**/**边缘端**多架构、多设备部署。
- **高性能**: 基于飞桨的高性能内核，模型训练速度及显存占用优势明显。支持FP16训练, 支持多机训练。
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/189026189-5d21e93a-5b33-40ce-bc36-c737122c1992.png" width="800"/>
</div>
## <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20"> 技术交流
- 如果你发现任何PaddleDetection存在的问题或者是建议, 欢迎通过[GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues)给我们提issues。
- **欢迎加入PaddleDetection 微信用户群（扫码填写问卷即可入群）**
- **入群福利 💎：获取PaddleDetection团队整理的重磅学习大礼包🎁**
- 📊 福利一：获取飞桨联合业界企业整理的开源数据集
- 👨‍🏫 福利二：获取PaddleDetection历次发版直播视频与最新直播咨询
- 🗳 福利三：获取垂类场景预训练模型集合，包括工业、安防、交通等5+行业场景
- 🗂 福利四：获取10+全流程产业实操范例，覆盖火灾烟雾检测、人流量计数等产业高频场景
<div align="center">
<img src="https://user-images.githubusercontent.com/34162360/177678712-4655747d-4290-4ad9-b7a1-4564a5418ac6.jpg" width = "200" />
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157827140-03ffaff7-7d14-48b4-9440-c38986ea378c.png" width="20"/> 套件结构概览
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Architectures</b>
</td>
<td>
<b>Backbones</b>
</td>
<td>
<b>Components</b>
</td>
<td>
<b>Data Augmentation</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<details open><summary><b>Object Detection</b></summary>
<ul>
<li>YOLOv3</li>
<li>YOLOv5</li>
<li>YOLOv6</li>
<li>YOLOv7</li>
<li>YOLOv8</li>
<li>PP-YOLOv1/v2</li>
<li>PP-YOLO-Tiny</li>
<li>PP-YOLOE</li>
<li>PP-YOLOE+</li>
<li>YOLOX</li>
<li>RTMDet</li>
</ul></details>
</ul>
</td>
<td>
<details open><summary><b>Details</b></summary>
<ul>
<li>ResNet(&vd)</li>
<li>CSPResNet</li>
<li>DarkNet</li>
<li>CSPDarkNet</li>
<li>ConvNeXt</li>
<li>EfficientRep</li>
<li>CSPBepBackbone</li>
<li>ELANNet</li>
<li>CSPNeXt</li>
</ul></details>
</td>
<td>
<details open><summary><b>Common</b></summary>
<ul>
<li>Sync-BN</li>
<li>Group Norm</li>
<li>DCNv2</li>
<li>EMA</li>
</ul> </details>
</ul>
<details open><summary><b>FPN</b></summary>
<ul>
<li>YOLOv3FPN</li>
<li>PPYOLOFPN</li>
<li>PPYOLOTinyFPN</li>
<li>PPYOLOPAN</li>
<li>YOLOCSPPAN</li>
<li>Custom-PAN</li>
<li>RepPAN</li>
<li>CSPRepPAN</li>
<li>ELANFPN</li>
<li>ELANFPNP6</li>
<li>CSPNeXtPAFPN</li>
</ul> </details>
</ul>
<details open><summary><b>Loss</b></summary>
<ul>
<li>Smooth-L1</li>
<li>GIoU/DIoU/CIoU</li>
<li>IoUAware</li>
<li>Focal Loss</li>
<li>VariFocal Loss</li>
</ul> </details>
</ul>
<details open><summary><b>Post-processing</b></summary>
<ul>
<li>SoftNMS</li>
<li>MatrixNMS</li>
</ul> </details>
</ul>
<details open><summary><b>Speed</b></summary>
<ul>
<li>FP16 training</li>
<li>Multi-machine training </li>
</ul> </details>
</ul>
</td>
<td>
<details open><summary><b>Details</b></summary>
<ul>
<li>Resize</li>
<li>Lighting</li>
<li>Flipping</li>
<li>Expand</li>
<li>Crop</li>
<li>Color Distort</li>
<li>Random Erasing</li>
<li>Mixup </li>
<li>AugmentHSV</li>
<li>Mosaic</li>
<li>Cutmix </li>
<li>Grid Mask</li>
<li>Auto Augment</li>
<li>Random Perspective</li>
</ul> </details>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
## <img src="https://user-images.githubusercontent.com/48054808/157801371-9a9a8c65-1690-4123-985a-e0559a7f9494.png" width="20"/> 模型性能概览
<details>
<summary><b> 云端模型性能对比</b></summary>
各模型结构和骨干网络的代表模型在COCO数据集上精度mAP和单卡Tesla V100上预测速度(FPS)对比图。
<div align="center">
<img src="docs/images/fps_map.png" />
</div>
**说明：**
- `PP-YOLOE`是对`PP-YOLO v2`模型的进一步优化，在COCO数据集精度51.6%，Tesla V100预测速度78.1FPS
- `PP-YOLOE+`是对`PPOLOE`模型的进一步优化，在COCO数据集精度53.3%，Tesla V100预测速度78.1FPS
- 图中模型均可在[模型库](#模型库)中获取
</details>
<details>
<summary><b> 移动端模型性能对比</b></summary>
各移动端模型在COCO数据集上精度mAP和高通骁龙865处理器上预测速度(FPS)对比图。
<div align="center">
<img src="docs/images/mobile_fps_map.png" width=600/>
</div>
**说明：**
- 测试数据均使用高通骁龙865(4\*A77 + 4\*A55)处理器batch size为1, 开启4线程测试，测试使用NCNN预测库，测试脚本见[MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
- [PP-PicoDet](configs/picodet)及[PP-YOLO-Tiny](configs/ppyolo)为PaddleDetection自研模型，其余模型PaddleDetection暂未提供
</details>
## <img src="https://user-images.githubusercontent.com/48054808/157829890-a535b8a6-631c-4c87-b861-64d4b32b2d6a.png" width="20"/> 模型库
<details>
<summary><b> 1. 通用检测</b></summary>
#### [PP-YOLOE+](./configs/ppyoloe)系列 推荐场景：Nvidia V100, T4等云端GPU和Jetson系列等边缘端设备
| 模型名称 | COCO精度（mAP） | V100 TensorRT FP16速度(FPS) | 配置文件 | 模型下载 |
|:---------- |:-----------:|:-------------------------:|:-----------------------------------------------------:|:------------------------------------------------------------------------------------:|
| PP-YOLOE+_s | 43.9 | 333.3 | [链接](configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) |
| PP-YOLOE+_m | 50.0 | 208.3 | [链接](configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) |
| PP-YOLOE+_l | 53.3 | 149.2 | [链接](configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) |
| PP-YOLOE+_x | 54.9 | 95.2 | [链接](configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) |
#### 前沿检测算法
| 模型名称 | COCO精度（mAP） | V100 TensorRT FP16速度(FPS) | 配置文件 | 模型下载 |
|:------------------------------------------------------------------ |:-----------:|:-------------------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|
| [YOLOX-l](configs/yolox) | 50.1 | 107.5 | [链接](configs/yolox/yolox_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) |
| [YOLOv5-l](configs/yolov5) | 48.6 | 136.0 | [链接](configs/yolov5/yolov5_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) |
| [YOLOv7-l](configs/yolov7) | 51.0 | 135.0 | [链接](configs/yolov7/yolov7_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov7_l_300e_coco.pdparams) |
</details>
## <img src="https://user-images.githubusercontent.com/48054808/157828296-d5eb0ccb-23ea-40f5-9957-29853d7d13a9.png" width="20"/> 文档教程
### 入门教程
- [安装说明](docs/tutorials/INSTALL_cn.md)
- [快速体验](docs/tutorials/QUICK_STARTED_cn.md)
- [数据准备](docs/tutorials/data/README.md)
- [PaddleDetection全流程使用](docs/tutorials/GETTING_STARTED_cn.md)
- [FAQ/常见问题汇总](docs/tutorials/FAQ)
### 进阶教程
- 参数配置
- [PP-YOLO参数说明](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
- 模型压缩(基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
- [剪裁/量化/蒸馏教程](configs/slim)
- [推理部署](deploy/README.md)
- [模型导出教程](deploy/EXPORT_MODEL.md)
- [Paddle Inference部署](deploy/README.md)
- [Python端推理部署](deploy/python)
- [C++端推理部署](deploy/cpp)
- [Paddle-Lite部署](deploy/lite)
- [Paddle Serving部署](deploy/serving)
- [ONNX模型导出](deploy/EXPORT_ONNX_MODEL.md)
- [推理benchmark](deploy/BENCHMARK_INFER.md)
- 进阶开发
- [数据处理模块](docs/advanced_tutorials/READER.md)
- [新增检测模型](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- 二次开发教程
- [目标检测](docs/advanced_tutorials/customization/detection.md)
## <img src="https://user-images.githubusercontent.com/48054808/157835981-ef6057b4-6347-4768-8fcc-cd07fcc3d8b0.png" width="20"/> 版本更新
版本更新内容请参考[版本更新文档](docs/CHANGELOG.md)
## <img title="" src="https://user-images.githubusercontent.com/48054808/157835345-f5d24128-abaf-4813-b793-d2e5bdc70e5a.png" alt="" width="20"> 许可证书
本项目的发布受[GPL-3.0 license](LICENSE)许可认证。
## <img src="https://user-images.githubusercontent.com/48054808/157835276-9aab9d1c-1c46-446b-bdd4-5ab75c5cfa48.png" width="20"/> 引用
```
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
README_en.md 0 → 100644
View file @ fccfdfa5
[简体中文](README_cn.md) | English
## Introduction
**PaddleYOLO** is a YOLO series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`YOLOv8`,`YOLOv5u`,`YOLOv7u`,`RTMDet` and so on, see COCO dataset ModelZoo in [ModelZoo](docs/MODEL_ZOO_en.md) and [configs](configs/).
<div align="center">
<img src="https://user-images.githubusercontent.com/13104100/213197403-c8257486-9ac4-486f-a0d5-4e3fe27ca852.jpg" width="480"/>
<img src="https://user-images.githubusercontent.com/13104100/213197635-eeb55433-bb2d-44f6-b374-73c616cfab24.jpg" width="480"/>
</div>
**Notes：**
- The Licence of **PaddleYOLO** is **[GPL 3.0](LICENSE)**, the codes of [YOLOv5](configs/yolov5),[YOLOv6](configs/yolov6),[YOLOv7](configs/yolov7) and [YOLOv8](configs/yolov8) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- To use **PaddleYOLO**, **PaddlePaddle-2.3.2 or above is recommended**，please refer to the [official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to download the appropriate version. **For Windows platforms, please install the paddle develop version**;
- **PaddleYOLO's [Roadmap](https://github.com/PaddlePaddle/PaddleYOLO/issues/44)** issue collects feature requests from user, welcome to put forward any opinions and suggestions.
## Tutorials
<details open>
<summary>Install</summary>
Clone repo and install [requirements.txt](./requirements.txt) in a
[**Python>=3.7.0**](https://www.python.org/) environment, including
[**PaddlePaddle>=2.3.2**](https://www.paddlepaddle.org.cn/install/).
```bash
git clone https://github.com/PaddlePaddle/PaddleYOLO # clone
cd PaddleYOLO
pip install -r requirements.txt # install
```
</details>
<details open>
<summary>Training/Evaluation/Inference</summary>
Write the following commands in a script file, such as ```run.sh```, and run as：```sh run.sh```. You can also run the command line sentence by sentence.
```bash
model_name=ppyoloe # yolov7
job_name=ppyoloe_plus_crn_s_80e_coco # yolov7_tiny_300e_coco
config=configs/${model_name}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 1.training（single GPU / multi GPU）
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amp
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.eval
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.infer
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5
# CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_dir=demo/ --draw_threshold=0.5
```
</details>
<details>
<summary>Deployment/Speed</summary>
Write the following commands in a script file, such as ```run.sh```, and run as：```sh run.sh```. You can also run the command line sentence by sentence.
```bash
model_name=ppyoloe # yolov7
job_name=ppyoloe_plus_crn_s_80e_coco # yolov7_tiny_300e_coco
config=configs/${model_name}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 4.export
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # trt=True
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_post_process=True # trt=True
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_nms=True # trt=True
# 5.deploy infer
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.deploy speed, add '--run_mode=trt_fp16' to test in TensorRT FP16 mode
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
# 7.export onnx
paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx
# 8.onnx speed
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp32
```
**Note：**
- If you want to switch models, just modify the first two lines, such as:
```
model_name=yolov7
job_name=yolov7_tiny_300e_coco
```
- For **exporting onnx**, you should install [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) by `pip install paddle2onnx` at first.
- For **FLOPs(G) and Params(M)**, you should install [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) by `pip install paddleslim` at first, then set `print_flops: True` and `print_params: True` in [runtime.yml](configs/runtime.yml). Make sure **single scale** like 640x640, **MACs are printed，FLOPs=2*MACs**.
</details>
<details open>
<summary> [Training Custom dataset](https://github.com/PaddlePaddle/PaddleYOLO/issues/43) </summary>
- Please refer to [doc](docs/MODEL_ZOO_en.md#CustomDataset) and [issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43).
- PaddleDetection team provides various **feature detection models based on PP-YOLOE** , which can also be used as a reference to modify on your custom dataset. Please refer to [PP-YOLOE application](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppyoloe/application), [pphuman](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/pphuman), [ppvehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/ppvehicle), [visdrone](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/visdrone) and [smalldet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/smalldet).
- PaddleDetection also provides **various YOLO models for VOC dataset** , which can also be used as a reference to modify on your custom dataset. Please refer to [voc](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/voc).
- Please **ensure the corresponding COCO trained weights are loaded as pre-train weights** at first. Set the `pretrain_weights: ` with corresponding COCO trained weights in the config file, and it will generally prompt that the number of channels convolved by the head classification layer does not correspond, which is a normal phenomenon, because the number of types of user-defined data sets is generally inconsistent with that of COCO data sets.
- We recommend to use YOLO detection model **with a total `batch_size` at least greater than `64` to train**. If the resources are insufficient, please **use the smaller model** or **reduce the input size of the model**. To ensure high detection accuracy, **you'd better not try to using single GPU or total `batch_size` less than `64` for training**;
</details>
## Updates
* 【2023/03/13】Support [YOLOv5u](configs/yolov5/yolov5u) and [YOLOv7u](configs/yolov7/yolov7u) inference and deploy;
* 【2023/01/10】Support [YOLOv8](configs/yolov8) inference and deploy;
* 【2022/09/29】Support [RTMDet](configs/rtmdet) inference and deploy;
* 【2022/09/26】Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO), see [ModelZoo](docs/MODEL_ZOO_en.md);
* 【2022/09/19】Support the new version of [YOLOv6](configs/yolov6), including n/t/s/m/l model;
* 【2022/08/23】Release `YOLOSeries` codebase: support `YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6` and `YOLOv7`; support using `ConvNeXt` backbone to get high-precision version of `PP-YOLOE`,`YOLOX` and `YOLOv5`; support PaddleSlim accelerated quantitative training `PP-YOLOE`,`YOLOv5`,`YOLOv6` and `YOLOv7`. For details, please read this [article](https://mp.weixin.qq.com/s/Hki01Zs2lQgvLSLWS0btrA)；
## <img src="https://user-images.githubusercontent.com/48054808/157793354-6e7f381a-0aa6-4bb7-845c-9acf2ecc05c3.png" width="20"/> Product Update
- 🔥 **2023.3.14：Release PaddleYOLO [release/2.6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6)**
- 💡 Model kit：
- Support `YOLOv8`,`YOLOv5u`,`YOLOv7u` inference and deploy.
- Support `Swin-Transformer`、`ViT`、`FocalNet` backbone to get high-precision version of `PP-YOLOE+`.
- Support `YOLOv8` in [FastDeploy](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/detection/paddledetection).
- 🔥 **2022.9.26：Release PaddleYOLO [release/2.5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5)**
- 💡 Model kit：
- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO): support `YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6` and `YOLOv7`; support using `ConvNeXt` backbone to get high-precision version of `PP-YOLOE`,`YOLOX` and `YOLOv5`; support PaddleSlim accelerated quantitative training `PP-YOLOE`,`YOLOv5`,`YOLOv6` and `YOLOv7`.
- 🔥 **2022.8.26：PaddleDetection [release/2.5 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5)**
- 🗳 Model features：
- Release [PP-YOLOE+](configs/ppyoloe): Increased accuracy by a maximum of 2.4% mAP to 54.9% mAP, 3.75 times faster model training convergence rate, and up to 2.3 times faster end-to-end inference speed; improved generalization for multiple downstream tasks
- Release [PicoDet-NPU](configs/picodet) model which supports full quantization deployment of models; add [PicoDet](configs/picodet) layout analysis model
- Release [PP-TinyPose Plus](./configs/keypoint/tiny_pose/). With 9.1% AP accuracy improvement in physical exercise, dance, and other scenarios, our PP-TinyPose Plus supports unconventional movements such as turning to one side, lying down, jumping, and high lifts
- 🔮 Functions in different scenarios
- Release the pedestrian analysis tool [PP-Human v2](./deploy/pipeline). It introduces four new behavior recognition: fighting, telephoning, smoking, and trespassing. The underlying algorithm performance is optimized, covering three core algorithm capabilities: detection, tracking, and attributes of pedestrians. Our model provides end-to-end development and model optimization strategies for beginners and supports online video streaming input.
- First release [PP-Vehicle](./deploy/pipeline), which has four major functions: license plate recognition, vehicle attribute analysis (color, model), traffic flow statistics, and violation detection. It is compatible with input formats, including pictures, online video streaming, and video. And we also offer our users a comprehensive set of tutorials for customization.
- 💡 Cutting-edge algorithms：
- Covers [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO) classic and latest models: YOLOv3, PP-YOLOE (a real-time high-precision object detection model developed by Baidu PaddlePaddle), and cutting-edge detection algorithms such as YOLOv4, YOLOv5, YOLOX, YOLOv6, and YOLOv7
- Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on COCO dataset; newly add multi-object tracking model [OC-SORT](configs/mot/ocsort); newly add [ConvNeXt](configs/convnext) backbone network.
- 📋 Industrial applications: Newly add [Smart Fitness](https://aistudio.baidu.com/aistudio/projectdetail/4385813), [Fighting recognition](https://aistudio.baidu.com/aistudio/projectdetail/4086987?channelType=0&channel=0),[ and Visitor Analysis](https://aistudio.baidu.com/aistudio/projectdetail/4230123?channelType=0&channel=0).
- 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.
- [More releases](https://github.com/PaddlePaddle/PaddleDetection/releases)
## <img title="" src="https://user-images.githubusercontent.com/48054808/157795569-9fc77c85-732f-4870-9be0-99a7fe2cff27.png" alt="" width="20"> Brief Introduction
**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.
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/189122825-ee1c1db2-b5f9-42c0-88b4-7975e1ec239d.gif" width="800"/>
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157799599-e6a66855-bac6-4e75-b9c0-96e13cb9612f.png" width="20"/> Features
- **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.
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/189066615-89d1dde2-54bc-4946-887e-fce50069206e.png" width="800"/>
</div>
## <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20"> Exchanges
- 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 user groups on WeChat (scan the QR code, add and reply "D" to the assistant)
<div align="center">
<img src="https://user-images.githubusercontent.com/34162360/177678712-4655747d-4290-4ad9-b7a1-4564a5418ac6.jpg" width = "200" />
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157827140-03ffaff7-7d14-48b4-9440-c38986ea378c.png" width="20"/> Kit Structure
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Architectures</b>
</td>
<td>
<b>Backbones</b>
</td>
<td>
<b>Components</b>
</td>
<td>
<b>Data Augmentation</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<details open><summary><b>Object Detection</b></summary>
<ul>
<li>YOLOv3</li>
<li>YOLOv5</li>
<li>YOLOv6</li>
<li>YOLOv7</li>
<li>YOLOv8</li>
<li>PP-YOLOv1/v2</li>
<li>PP-YOLO-Tiny</li>
<li>PP-YOLOE</li>
<li>PP-YOLOE+</li>
<li>YOLOX</li>
<li>RTMDet</li>
</ul></details>
</ul>
</td>
<td>
<details open><summary><b>Details</b></summary>
<ul>
<li>ResNet(&vd)</li>
<li>CSPResNet</li>
<li>DarkNet</li>
<li>CSPDarkNet</li>
<li>ConvNeXt</li>
<li>EfficientRep</li>
<li>CSPBepBackbone</li>
<li>ELANNet</li>
<li>CSPNeXt</li>
</ul></details>
</td>
<td>
<details open><summary><b>Common</b></summary>
<ul>
<li>Sync-BN</li>
<li>Group Norm</li>
<li>DCNv2</li>
<li>EMA</li>
</ul> </details>
</ul>
<details open><summary><b>FPN</b></summary>
<ul>
<li>YOLOv3FPN</li>
<li>PPYOLOFPN</li>
<li>PPYOLOTinyFPN</li>
<li>PPYOLOPAN</li>
<li>YOLOCSPPAN</li>
<li>Custom-PAN</li>
<li>RepPAN</li>
<li>CSPRepPAN</li>
<li>ELANFPN</li>
<li>ELANFPNP6</li>
<li>CSPNeXtPAFPN</li>
</ul> </details>
</ul>
<details open><summary><b>Loss</b></summary>
<ul>
<li>Smooth-L1</li>
<li>GIoU/DIoU/CIoU</li>
<li>IoUAware</li>
<li>Focal Loss</li>
<li>VariFocal Loss</li>
</ul> </details>
</ul>
<details open><summary><b>Post-processing</b></summary>
<ul>
<li>SoftNMS</li>
<li>MatrixNMS</li>
</ul> </details>
</ul>
<details open><summary><b>Speed</b></summary>
<ul>
<li>FP16 training</li>
<li>Multi-machine training </li>
</ul> </details>
</ul>
</td>
<td>
<details open><summary><b>Details</b></summary>
<ul>
<li>Resize</li>
<li>Lighting</li>
<li>Flipping</li>
<li>Expand</li>
<li>Crop</li>
<li>Color Distort</li>
<li>Random Erasing</li>
<li>Mixup </li>
<li>AugmentHSV</li>
<li>Mosaic</li>
<li>Cutmix </li>
<li>Grid Mask</li>
<li>Auto Augment</li>
<li>Random Perspective</li>
</ul> </details>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
## <img src="https://user-images.githubusercontent.com/48054808/157801371-9a9a8c65-1690-4123-985a-e0559a7f9494.png" width="20"/> Model Performance
<details>
<summary><b> Performance comparison of Cloud models</b></summary>
The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
<div align="center">
<img src="docs/images/fps_map.png" />
</div>
**Clarification：**
- `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
- `PP-YOLOE+` are optimized `PP-YOLOE`. It reached accuracy as 53.3% 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)
</details>
<details>
<summary><b> Performance omparison on mobiles</b></summary>
The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices.
<div align="center">
<img src="docs/images/mobile_fps_map.png" width=600/>
</div>
**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.
</details>
## <img src="https://user-images.githubusercontent.com/48054808/157829890-a535b8a6-631c-4c87-b861-64d4b32b2d6a.png" width="20"/> Model libraries
<details>
<summary><b> 1. General detection</b></summary>
#### 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 | 43.9 | 333.3 | [link](configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) |
| PP-YOLOE+_m | 50.0 | 208.3 | [link](configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) |
| PP-YOLOE+_l | 53.3 | 149.2 | [link](configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) |
| PP-YOLOE+_x | 54.9 | 95.2 | [link](configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) |
#### Frontier detection algorithm
| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 speed(FPS) | Configuration | Download |
|:-------- |:------------------:|:-----------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------:|
| [YOLOX-l](configs/yolox) | 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](configs/yolov5) | 48.6 | 136.0 | [Link](configs/yolov5/yolov5_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) |
| [YOLOv7-l](configs/yolov7) | 51.0 | 135.0 | [链接](configs/yolov7/yolov7_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov7_l_300e_coco.pdparams) |
</details>
## <img src="https://user-images.githubusercontent.com/48054808/157828296-d5eb0ccb-23ea-40f5-9957-29853d7d13a9.png" width="20"/>Document tutorials
### Introductory tutorials
- [Installation](docs/tutorials/INSTALL_cn.md)
- [Quick start](docs/tutorials/QUICK_STARTED_cn.md)
- [Data preparation](docs/tutorials/data/README.md)
- [Geting Started on PaddleDetection](docs/tutorials/GETTING_STARTED_cn.md)
- [FAQ]((docs/tutorials/FAQ)
### Advanced tutorials
- Configuration
- [PP-YOLO Configuration](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
- Compression based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
- [Pruning/Quantization/Distillation Tutorial](configs/slim)
- [Inference deployment](deploy/README.md)
- [Export model for inference](deploy/EXPORT_MODEL.md)
- [Paddle Inference deployment](deploy/README.md)
- [Inference deployment with Python](deploy/python)
- [Inference deployment with C++](deploy/cpp)
- [Paddle-Lite deployment](deploy/lite)
- [Paddle Serving deployment](deploy/serving)
- [ONNX model export](deploy/EXPORT_ONNX_MODEL.md)
- [Inference benchmark](deploy/BENCHMARK_INFER.md)
- Advanced development
- [Data processing module](docs/advanced_tutorials/READER.md)
- [New object detection models](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- Custumization
- [Object detection](docs/advanced_tutorials/customization/detection.md)
## <img src="https://user-images.githubusercontent.com/48054808/157835981-ef6057b4-6347-4768-8fcc-cd07fcc3d8b0.png" width="20"/> Version updates
Please refer to the[ Release note ](https://github.com/PaddlePaddle/Paddle/wiki/PaddlePaddle-2.3.0-Release-Note-EN)for more details about the updates
## <img title="" src="https://user-images.githubusercontent.com/48054808/157835345-f5d24128-abaf-4813-b793-d2e5bdc70e5a.png" alt="" width="20"> License
PaddleYOLO is provided under the [GPL-3.0 license](LICENSE)
## <img src="https://user-images.githubusercontent.com/48054808/157835276-9aab9d1c-1c46-446b-bdd4-5ab75c5cfa48.png" width="20"/> Quote
```
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
asserts/model.properties 0 → 100644
View file @ fccfdfa5
# 模型唯一标识
modelCode = 469
# 模型名称
modelName=yolov5_paddle
# 模型描述
modelDescription=yolov5是一种基于深度学习的目标检测算法,可以广泛应用于各种计算机视觉和人工智能领域的应用中
# 应用场景
appScenario=推理,训练,金融,交通,教育
# 框架类型
frameType=paddle
\ No newline at end of file
configs/datasets/coco_detection.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 80
TrainDataset:
name: COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
name: COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
TestDataset:
name: ImageFolder
anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
configs/datasets/objects365_detection.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 365
TrainDataset:
!COCODataSet
image_dir: train
anno_path: annotations/zhiyuan_objv2_train.json
dataset_dir: dataset/objects365
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
image_dir: val
anno_path: annotations/zhiyuan_objv2_val.json
dataset_dir: dataset/objects365
allow_empty: true
TestDataset:
!ImageFolder
anno_path: annotations/zhiyuan_objv2_val.json
dataset_dir: dataset/objects365/
configs/datasets/roadsign_voc.yml 0 → 100644
View file @ fccfdfa5
metric: VOC
map_type: integral
num_classes: 4
TrainDataset:
name: VOCDataSet
dataset_dir: dataset/roadsign_voc
anno_path: train.txt
label_list: label_list.txt
data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
EvalDataset:
name: VOCDataSet
dataset_dir: dataset/roadsign_voc
anno_path: valid.txt
label_list: label_list.txt
data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
TestDataset:
name: ImageFolder
anno_path: dataset/roadsign_voc/label_list.txt
configs/datasets/visdrone_detection.yml 0 → 100644
View file @ fccfdfa5
metric: COCO
num_classes: 10
TrainDataset:
!COCODataSet
image_dir: VisDrone2019-DET-train
anno_path: train.json
dataset_dir: dataset/visdrone
data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
EvalDataset:
!COCODataSet
image_dir: VisDrone2019-DET-val
anno_path: val.json
# image_dir: test_dev
# anno_path: test_dev.json
dataset_dir: dataset/visdrone
TestDataset:
!ImageFolder
anno_path: val.json
dataset_dir: dataset/visdrone
configs/datasets/voc.yml 0 → 100644
View file @ fccfdfa5
metric: VOC
map_type: 11point
num_classes: 20
TrainDataset:
name: VOCDataSet
dataset_dir: dataset/voc
anno_path: trainval.txt
label_list: label_list.txt
data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
EvalDataset:
name: VOCDataSet
dataset_dir: dataset/voc
anno_path: test.txt
label_list: label_list.txt
data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
TestDataset:
name: ImageFolder
anno_path: dataset/voc/label_list.txt
configs/runtime.yml 0 → 100644
View file @ fccfdfa5
use_gpu: true
use_xpu: false
use_mlu: false
use_npu: false
log_iter: 20
save_dir: output
snapshot_epoch: 1
print_flops: false
print_params: false
# Exporting the model
export:
post_process: True # Whether post-processing is included in the network when export model.
nms: True # Whether NMS is included in the network when export model.
benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
fuse_conv_bn: False
configs/yolov5/README.md 0 → 100644
View file @ fccfdfa5
# YOLOv5
## 内容
- [模型库](#模型库)
- [使用说明](#使用说明)
- [速度测试](#速度测试)
## 模型库
### 基础模型
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5-n | 640 | 16 | 300e | 1.5 | 28.0 | 45.7 | 1.87 | 4.52 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_n_300e_coco.pdparams) | [配置文件](./yolov5_n_300e_coco.yml) |
| YOLOv5-s | 640 | 16 | 300e | 2.6 | 37.6 | 56.7 | 7.24 | 16.54 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams) | [配置文件](./yolov5_s_300e_coco.yml) |
| YOLOv5-m | 640 | 16 | 300e | 5.2 | 45.4 | 64.1 | 21.19 | 49.08 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_m_300e_coco.pdparams) | [配置文件](./yolov5_m_300e_coco.yml) |
| YOLOv5-l | 640 | 16 | 300e | 7.9 | 48.9 | 67.1 | 46.56 | 109.32 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) | [配置文件](./yolov5_l_300e_coco.yml) |
| YOLOv5-x | 640 | 16 | 300e | 13.7 | 50.6 | 68.7 | 86.75 | 205.92 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_x_300e_coco.pdparams) | [配置文件](./yolov5_x_300e_coco.yml) |
| YOLOv5-s ConvNeXt| 640 | 8 | 36e | - | 42.4 | 65.3 | 34.54 | 17.96 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_convnext_s_36e_coco.pdparams) | [配置文件](../convnext/yolov5_convnext_s_36e_coco.yml) |
### SSOD预训练模型
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5-s | 640 | 16 | 80e | 2.6 | 38.8 | 58.2 | 7.24 | 16.54 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_s_80e_ssod_finetune_coco.pdparams) | [配置文件](./yolov5_s_80e_ssod_finetune_coco.yml) |
### Objects-365预训练模型
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5-l | 640 | 16 | 30e | 7.9 | 49.3 | 67.8 | 46.56 | 109.32 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_l_30e_obj365_finetune_coco.pdparams) | [配置文件](./yolov5_l_30e_obj365_finetune_coco.yml) |
### P6大尺度模型
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5p6-n | 1280 | 16 | 300e | - | 35.9 | 54.2 | 3.25 | 9.23 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5p6_n_300e_coco.pdparams) | [配置文件](./yolov5p6_n_300e_coco.yml) |
| YOLOv5p6-s | 1280 | 16 | 300e | - | 44.5 | 63.3 | 12.63 | 33.81 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5p6_s_300e_coco.pdparams) | [配置文件](./yolov5p6_s_300e_coco.yml) |
| YOLOv5p6-m | 1280 | 16 | 300e | - | 51.1 | 69.0 | 35.73 | 100.21 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5p6_m_300e_coco.pdparams) | [配置文件](./yolov5p6_m_300e_coco.yml) |
| YOLOv5p6-l | 1280 | 8 | 300e | - | 53.4 | 71.0 | 76.77 | 223.09 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5p6_l_300e_coco.pdparams) | [配置文件](./yolov5p6_l_300e_coco.yml) |
| YOLOv5p6-x | 1280 | 8 | 300e | - | 54.7 | 72.4 | 140.80 | 420.03 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5p6_x_300e_coco.pdparams) | [配置文件](./yolov5p6_x_300e_coco.yml) |
### [YOLOv5u](../yolov5u)
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5u-n | 640 | 16 | 300e | 1.61 | 34.5 | 49.7 | 2.65 | 7.79 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_n_300e_coco.pdparams) | [配置文件](./yolov5u/yolov5u_n_300e_coco.yml) |
| YOLOv5u-s | 640 | 16 | 300e | 2.66 | 43.0 | 59.7 | 9.15 | 24.12 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_s_300e_coco.pdparams) | [配置文件](./yolov5u/yolov5u_s_300e_coco.yml) |
| YOLOv5u-m | 640 | 16 | 300e | 5.50 | 49.0 | 65.7 | 25.11 | 64.42 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_m_300e_coco.pdparams) | [配置文件](./yolov5u/yolov5u_m_300e_coco.yml) |
| YOLOv5u-l | 640 | 16 | 300e | 8.73 | 52.2 | 69.0 | 53.23 | 135.34 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_l_300e_coco.pdparams) | [配置文件](./yolov5u/yolov5u_l_300e_coco.yml) |
| YOLOv5u-x | 640 | 16 | 300e | 15.49 | 53.1 | 69.9 | 97.28 | 246.89 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_x_300e_coco.pdparams) | [配置文件](./yolov5u/yolov5u_x_300e_coco.yml) |
**注意:**
- YOLOv5模型训练使用COCO train2017作为训练集，Box AP为在COCO val2017上的`mAP(IoU=0.5:0.95)`结果；
- YOLOv5u 模型表示YOLOv5结构使用YOLOv8的head和loss，是Anchor Free的检测方案，具体可参照[YOLOv5u](../yolov5u)；
- YOLOv5模型训练过程中默认使用8 GPUs进行混合精度训练，默认lr为0.01为8卡总batch_size的设置，如果**GPU卡数**或者每卡**batch size**发生改动，也不需要改动学习率，但为了保证高精度最好使用**总batch size大于64**的配置去训练；
- 模型推理耗时(ms)为TensorRT-FP16下测试的耗时，不包含数据预处理和模型输出后处理(NMS)的耗时。测试采用单卡Tesla T4 GPU，batch size=1，测试环境为**paddlepaddle-2.3.2**, **CUDA 11.2**, **CUDNN 8.2**, **GCC-8.2**, **TensorRT 8.0.3.4**，具体请参考[速度测试](#速度测试)。
- 如果你设置了`--run_benchmark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
### 部署模型
| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv5-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_wo_nms.onnx) |
| YOLOv5-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_wo_nms.onnx) |
| YOLOv5-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_wo_nms.onnx) |
| YOLOv5-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_wo_nms.onnx) |
| YOLOv5-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_wo_nms.onnx) |
## 使用教程
### 0. **一键运行全流程**
将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行。
```bash
model_name=yolov5 # 可修改，如 ppyoloe
job_name=yolov5_s_300e_coco # 可修改，如 ppyoloe_plus_crn_s_80e_coco
config=configs/${model_name}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 1.训练（单卡/多卡），加 --eval 表示边训边评估，加 --amp 表示混合精度训练
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amp
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.评估，加 --classwise 表示输出每一类mAP
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.预测 (单张图/图片文件夹）
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5
# CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_dir=demo/ --draw_threshold=0.5
# 4.导出模型，以下3种模式选一种
## 普通导出，加trt表示用于trt加速，对NMS和silu激活函数提速明显
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # trt=True
## exclude_post_process去除后处理导出，返回和YOLOv5导出ONNX时相同格式的concat后的1个Tensor，是未缩放回原图的坐标+分类置信度
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_post_process=True # trt=True
## exclude_nms去除NMS导出，返回2个Tensor，是缩放回原图后的坐标和分类置信度
# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_nms=True # trt=True
# 5.部署预测，注意不能使用 去除后处理 或 去除NMS 导出后的模型去预测
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.部署测速，加 “--run_mode=trt_fp16” 表示在TensorRT FP16模式下测速，注意如需用到 trt_fp16 则必须为加 trt=True 导出的模型
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
# 7.onnx导出，一般结合 exclude_post_process去除后处理导出的模型
paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx
# 8.onnx trt测速
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp32
```
### 1. 训练
执行以下指令使用混合精度训练YOLOv5
```bash
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/yolov5/yolov5_s_300e_coco.yml --amp --eval
```
**注意:**
- `--amp`表示开启混合精度训练以避免显存溢出，`--eval`表示边训边验证。
### 2. 评估
执行以下命令在单个GPU上评估COCO val2017数据集
```bash
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams
```
### 3. 推理
使用以下命令在单张GPU上预测图片，使用`--infer_img`推理单张图片以及使用`--infer_dir`推理文件中的所有图片。
```bash
# 推理单张图片
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
# 推理文件中的所有图片
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams --infer_dir=demo
```
### 4.导出模型
YOLOv5在GPU上推理部署或benchmark测速等需要通过`tools/export_model.py`导出模型。
当你**使用Paddle Inference但不使用TensorRT**时，运行以下的命令导出模型
```bash
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams
```
当你**使用Paddle Inference且使用TensorRT**时，需要指定`-o trt=True`来导出模型。
```bash
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams trt=True
```
如果你想将YOLOv5模型导出为**ONNX格式**，参考
[PaddleDetection模型导出为ONNX格式教程](../../deploy/EXPORT_ONNX_MODEL.md)，运行以下命令：
```bash
# 导出推理模型
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams
# 安装paddle2onnx
pip install paddle2onnx
# 转换成onnx格式
paddle2onnx --model_dir output_inference/yolov5_s_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file yolov5_s_300e_coco.onnx
```
**注意：** ONNX模型目前只支持batch_size=1
### 5.推理部署
YOLOv5可以使用以下方式进行部署：
- Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
- [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
- [PaddleServing](https://github.com/PaddlePaddle/Serving)
- [PaddleSlim模型量化](../slim)
运行以下命令导出模型
```bash
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams trt=True
```
**注意：**
- trt=True表示**使用Paddle Inference且使用TensorRT**进行测速，速度会更快，默认不加即为False，表示**使用Paddle Inference但不使用TensorRT**进行测速。
- 如果是使用Paddle Inference在TensorRT FP16模式下部署，需要参考[Paddle Inference文档](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python)，下载并安装与你的CUDA, CUDNN和TensorRT相应的wheel包。
#### 5.1.Python部署
`deploy/python/infer.py`使用上述导出后的Paddle Inference模型用于推理和benchnark测速，如果设置了`--run_benchmark=True`, 首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
```bash
# Python部署推理单张图片
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
# 推理文件夹下的所有图片
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_dir=demo/ --device=gpu
```
#### 5.2. C++部署
`deploy/cpp/build/main`使用上述导出后的Paddle Inference模型用于C++推理部署, 首先按照[docs](../../deploy/cpp/docs)编译安装环境。
```bash
# C++部署推理单张图片
./deploy/cpp/build/main --model_dir=output_inference/yolov5_s_300e_coco/ --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=GPU --threshold=0.5 --output_dir=cpp_infer_output/yolov5_s_300e_coco
```
## 速度测试
为了公平起见，在[模型库](#模型库)中的速度测试结果均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)，需要在导出模型时指定`-o exclude_nms=True`。测速需设置`--run_benchmark=True`, 首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
**使用Paddle Inference但不使用TensorRT**进行测速，执行以下命令：
```bash
# 导出模型
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams exclude_nms=True
# 速度测试，使用run_benchmark=True
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True
```
**使用Paddle Inference且使用TensorRT**进行测速，执行以下命令：
```bash
# 导出模型，使用trt=True
python tools/export_model.py -c configs/yolov5/yolov5_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams exclude_nms=True trt=True
# 速度测试，使用run_benchmark=True
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
# tensorRT-FP32测速
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp32
# tensorRT-FP16测速
python deploy/python/infer.py --model_dir=output_inference/yolov5_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16
```
**注意:**
- 导出模型时指定`-o exclude_nms=True`仅作为测速时用，这样导出的模型其推理部署预测的结果不是最终检出框的结果。
- [模型库](#模型库)中的速度测试结果为tensorRT-FP16测速后的最快速度，为不包含数据预处理和模型输出后处理(NMS)的耗时。
configs/yolov5/_base_/optimizer_300e.yml 0 → 100644
View file @ fccfdfa5
epoch: 300
LearningRate:
base_lr: 0.01
schedulers:
- !YOLOv5LRDecay
max_epochs: 300
min_lr_ratio: 0.01
- !ExpWarmup
epochs: 3
OptimizerBuilder:
optimizer:
type: Momentum
momentum: 0.937
use_nesterov: True
regularizer:
factor: 0.0005
type: L2
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