+ 
+ 
+
+
+**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
+
+
+
+
+
+
+安装
+ +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 +``` + +
+
+
+训练/验证/预测/
+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 +``` + +
+
+
+
+部署/测速
+ +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**。 + +
+
+
+
+## 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)；
+
+
+## [训练自定义数据集](https://github.com/PaddlePaddle/PaddleYOLO/issues/43)
+ +- 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**； + +
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)
+
+## 
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。
+
+
+ 
+
+
+##
+
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。
+
+
+ 
+
+
+##
+
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
+
+ 
+
+
+##
+ 
套件结构概览
+
+| + Architectures + | ++ Backbones + | ++ Components + | ++ Data Augmentation + | +
+
Object Detection+
|
+
+ Details+
|
+
+ Common+
FPN+
Loss+
Post-processing+
Speed+
|
+
+ Details+
|
+
模型性能概览
+
+
+
+
+云端模型性能对比
+ +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。 + +
+ 
+
+
+**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
+
+
+
+
+
+## 移动端模型性能对比
+ +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。 + +
+ 
+
+
+**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
+
+
+
模型库
+
+
+
+
+
+## 1. 通用检测
+ +#### [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) | + +
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)
+
+
+## 
版本更新
+
+版本更新内容请参考[版本更新文档](docs/CHANGELOG.md)
+
+
+## 
许可证书
+
+本项目的发布受[GPL-3.0 license](LICENSE)许可认证。
+
+
+## 
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}
+}
+```
diff --git a/README_en.md b/README_en.md
new file mode 100644
index 0000000..947c8fe
--- /dev/null
+++ b/README_en.md
@@ -0,0 +1,446 @@
+[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/).
+
+
+
+
+
+
+**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
+
+
+
+
+
+
+
+Install
+ +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 +``` + +
+
+
+
+Training/Evaluation/Inference
+ +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 +``` + +
+
+
+
+Deployment/Speed
+ +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**. + +
+
+
+
+## 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)；
+
+
+## [Training Custom dataset](https://github.com/PaddlePaddle/PaddleYOLO/issues/43)
+ +- 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**; + + 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)
+
+## 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.
+
+
+ 
+
+
+##
+ 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.
+
+
+ 
+
+
+##
+ 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)
+
+
+
+
+
+##
+ Kit Structure
+
+| + Architectures + | ++ Backbones + | ++ Components + | ++ Data Augmentation + | +
+
Object Detection+
|
+
+ Details+
|
+
+ Common+
FPN+
Loss+
Post-processing+
Speed+
|
+
+ Details+
|
+
Model Performance
+
+
+
+
+Performance comparison of Cloud models
+ +The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones. + +
+
+
+
+**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)
+
+
+
+
+
+## Performance omparison on mobiles
+ +The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices. + +
+
+
+
+**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 | 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) | + +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)
+
+
+## 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
+
+## License
+
+PaddleYOLO is provided under the [GPL-3.0 license](LICENSE)
+
+## 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}
+}
+```
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