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configs/ppyoloe/README.md

+English | [简体中文](README_cn.md)
+
+# PP-YOLOE
+
+## Table of Contents
+- [Introduction](#Introduction)
+- [Model Zoo](#Model-Zoo)
+- [Getting Start](#Getting-Start)
+- [Appendix](#Appendix)
+
+## Introduction
+PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular yolo models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as deformable convolution or matrix nms, to be deployed friendly on various hardware. For more details, please refer to our report.
+
+<div align="center">
+  <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
+</div>
+
+PP-YOLOE-l achieves 51.4 mAP on COCO test-dev2017 dataset with 78.1 FPS on Tesla V100. While using TensorRT FP16, PP-YOLOE-l can be further accelerated to 149.2 FPS. PP-YOLOE-s/m/x also have excellent accuracy and speed performance, which can be found in [Model Zoo](#Model-Zoo)
+
+PP-YOLOE is composed of following methods:
+- Scalable backbone and neck
+- [Task Alignment Learning](https://arxiv.org/abs/2108.07755)
+- Efficient Task-aligned head with [DFL](https://arxiv.org/abs/2006.04388) and [VFL](https://arxiv.org/abs/2008.13367)
+- [SiLU activation function](https://arxiv.org/abs/1710.05941)
+
+## Model Zoo
+|          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
+|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       42.7        |        43.1         |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  |     8      |     32     | cspresnet-m |     640     |       48.6        |        48.9         |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  |     8      |     24     | cspresnet-l |     640     |       50.9        |        51.4         |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       51.9        |        52.2         |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
+
+**Notes:**
+
+- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
+- PP-YOLOE used 8 GPUs for mixed precision training, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
+- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, CUDA 10.2, CUDNN 7.6.5, TensorRT 6.0.1.8 in TensorRT mode.
+- PP-YOLOE inference speed testing uses inference model exported by `tools/export_model.py` with `-o exclude_nms=True` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
+- If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
+
+## Getting Start
+
+### 1. Training
+
+Training PP-YOLOE with mixed precision on 8 GPUs with following command
+
+```bash
+python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp
+```
+
+** Notes: ** use `--amp` to train with default config to avoid out of memeory.
+
+### 2. Evaluation
+
+Evaluating PP-YOLOE on COCO val2017 dataset in single GPU with following commands:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+```
+
+For evaluation on COCO test-dev2017 dataset, please download COCO test-dev2017 dataset from [COCO dataset download](https://cocodataset.org/#download) and decompress to COCO dataset directory and configure `EvalDataset` like `configs/ppyolo/ppyolo_test.yml`.
+
+### 3. Inference
+
+Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.
+
+```bash
+# inference single image
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg
+
+# inference all images in the directory
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams --infer_dir=demo
+```
+
+### 4. Deployment
+
+For deployment on GPU or benchmarked, model should be first exported to inference model using `tools/export_model.py`.
+
+Exporting PP-YOLOE for Paddle Inference **without TensorRT**, use following command.
+
+```bash
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+```
+
+Exporting PP-YOLOE for Paddle Inference **with TensorRT** for better performance, use following command with extra `-o trt=True` setting.
+
+```bash
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams -o trt=True
+```
+
+`deploy/python/infer.py` is used to load exported paddle inference model above for inference and benchmark through PaddleInference.
+
+```bash
+# inference single image
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
+
+# inference all images in the directory
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_dir=demo/ --device=gpu
+
+# benchmark
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
+```
+
+If you want to export PP-YOLOE model to **ONNX format**, use following command refer to [PaddleDetection Model Export as ONNX Format Tutorial](../../deploy/EXPORT_ONNX_MODEL_en.md).
+
+```bash
+# export inference model
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+
+# install paddle2onnx
+pip install paddle2onnx
+
+# convert to onnx
+paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
+
+```
+
+## Appendix
+
+Ablation experiments of PP-YOLOE.
+
+| NO.  |        Model                 | Box AP<sup>val</sup> | Params(M) | FLOPs(G) | V100 FP32 FPS |
+| :--: | :---------------------------: | :------------------: | :-------: | :------: | :-----------: |
+|  A   | PP-YOLOv2          |         49.1         |   54.58   |  115.77   |     68.9     |
+|  B   | A + Anchor-free    |         48.8         |   54.27   |  114.78   |     69.8     |
+|  C   | B + CSPRepResNet   |         49.5         |   47.42   |  101.87   |     85.5     |
+|  D   | C + TAL            |         50.4         |   48.32   |  104.75   |     84.0     |
+|  E   | D + ET-Head        |         50.9         |   52.20   |  110.07   |     78.1     |

configs/ppyoloe/README_cn.md

+简体中文 | [English](README.md)
+
+# PP-YOLOE
+
+## 内容
+- [简介](#简介)
+- [模型库](#模型库)
+- [使用说明](#使用说明)
+- [附录](#附录)
+
+## 简介
+PP-YOLOE是基于PP-YOLOv2的卓越的单阶段Anchor-free模型，超越了多种流行的yolo模型。PP-YOLOE有一系列的模型，即s/m/l/x，可以通过width multiplier和depth multiplier配置。PP-YOLOE避免使用诸如deformable convolution或者matrix nms之类的特殊算子，以使其能轻松地部署在多种多样的硬件上。更多细节可以参考我们的report。
+
+<div align="center">
+  <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
+</div>
+
+PP-YOLOE-l在COCO test-dev2017达到了51.4的mAP, 同时其速度在Tesla V100上达到了78.1 FPS。PP-YOLOE-s/m/x同样具有卓越的精度速度性价比, 其精度速度可以在[模型库](#模型库)中找到。
+
+PP-YOLOE由以下方法组成
+- 可扩展的backbone和neck
+- [Task Alignment Learning](https://arxiv.org/abs/2108.07755)
+- Efficient Task-aligned head with [DFL](https://arxiv.org/abs/2006.04388)和[VFL](https://arxiv.org/abs/2008.13367)
+- [SiLU激活函数](https://arxiv.org/abs/1710.05941)
+
+## 模型库
+|          模型           | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件  |
+|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       42.7        |        43.1         |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  |     8      |     32     | cspresnet-m |     640     |       48.6        |        48.9         |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  |     8      |     24     | cspresnet-l |     640     |       50.9        |        51.4         |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       51.9        |        52.2         |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml)                   |
+
+**注意:**
+
+- PP-YOLOE模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
+- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
+- PP-YOLOE模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.6.5，TensorRT推理速度测试使用TensorRT 6.0.1.8。
+- PP-YOLOE推理速度测试使用`tools/export_model.py`并设置`-o exclude_nms=True`脚本导出的模型，并用`deploy/python/infer.py`设置`--run_benchnark`参数得到。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)。
+- 如果你设置了`--run_benchnark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
+
+## 使用教程
+
+### 1. 训练
+
+执行以下指令使用混合精度训练PP-YOLOE
+
+```bash
+python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp
+```
+
+** 注意: ** 使用默认配置训练需要设置`--amp`以避免显存溢出.
+
+### 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/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+```
+
+在coco test-dev2017上评估，请先从[COCO数据集下载](https://cocodataset.org/#download)下载COCO test-dev2017数据集，然后解压到COCO数据集文件夹并像`configs/ppyolo/ppyolo_test.yml`一样配置`EvalDataset`。
+
+### 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/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_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/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams --infer_dir=demo
+```
+
+### 4. 部署
+
+PP-YOLOE在GPU上部署或者推理benchmark需要通过`tools/export_model.py`导出模型。
+
+当你使用PaddleInferenced但不使用TensorRT时，运行以下的命令进行导出
+
+```bash
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+```
+
+当你使用PaddleInference的TensorRT时，需要指定`-o trt=True`进行导出
+
+```bash
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams -o trt=True
+```
+
+`deploy/python/infer.py`使用上述导出后的PaddleInference模型用于推理和benchnark.
+
+```bash
+# 推理单张图片
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu
+
+# 推理文件夹下的所有图片
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_dir=demo/ --device=gpu
+
+# benchmark
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyolo_r50vd_dcn_1x_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True
+```
+
+如果你想将PP-YOLOE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)
+
+
+```bash
+# 导出推理模型
+python tools/export_model.py configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+
+# 安装paddle2onnx
+pip install paddle2onnx
+
+# 转换成onnx格式
+paddle2onnx --model_dir output_inference/ppyoloe_crn_l_300e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_crn_l_300e_coco.onnx
+
+```
+
+## 附录
+
+PP-YOLOE消融实验
+
+| 序号 |        模型                  | Box AP<sup>val</sup> | 参数量(M) | FLOPs(G) | V100 FP32 FPS |
+| :--: | :---------------------------: | :-------------------: | :-------: | :------: | :-----------: |
+|  A   | PP-YOLOv2          |         49.1         |   54.58   |  115.77   |     68.9     |
+|  B   | A + Anchor-free    |         48.8         |   54.27   |  114.78   |     69.8     |
+|  C   | B + CSPRepResNet   |         49.5         |   47.42   |  101.87   |     85.5     |
+|  D   | C + TAL            |         50.4         |   48.32   |  104.75   |     84.0     |
+|  E   | D + ET-Head        |         50.9         |   52.20   |  110.07   |     78.1     |