English | [简体中文](README_cn.md) # PP-YOLOE ## Latest News - Release PP-YOLOE+ model: **(2022.08)** - Pre training model using large-scale data set obj365 - In the backbone, add the alpha parameter to the block branch - Optimize the end-to-end inference speed and improve the training convergence speed ## Legacy model - Please refer to：[PP-YOLOE 2022.03](./README_legacy.md) for details ## 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](https://arxiv.org/abs/2203.16250).
PP-YOLOE+_l achieves 53.3 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(Swish) activation function](https://arxiv.org/abs/1710.05941) ## Model Zoo ### Model Zoo on COCO | Model | Epoch | GPU number | images/GPU | backbone | input shape | Box APval
0.5:0.95 | Box APtest
0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config | |:--------------:|:-----:|:-------:|:----------:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:|:---------------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:| | PP-YOLOE+_s | 80 | 8 | 8 | cspresnet-s | 640 | 43.7 | 43.9 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_s_80e_coco.yml) | | PP-YOLOE+_m | 80 | 8 | 8 | cspresnet-m | 640 | 49.8 | 50.0 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_m_80e_coco.yml) | | PP-YOLOE+_l | 80 | 8 | 8 | cspresnet-l | 640 | 52.9 | 53.3 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_l_80e_coco.yml) | | PP-YOLOE+_x | 80 | 8 | 8 | cspresnet-x | 640 | 54.7 | 54.9 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_x_80e_coco.yml) | ### Comprehensive Metrics | Model | Epoch | AP0.5:0.95 | AP0.5 | AP0.75 | APsmall | APmedium | APlarge | ARsmall | ARmedium | ARlarge | |:------------------------:|:-----:|:---------------:|:----------:|:------------:|:------------:| :-----------: |:------------:|:------------:|:-------------:|:------------:| | PP-YOLOE+_s | 80 | 43.7 | 60.6 | 47.9 | 26.5 | 47.5 | 59.0 | 46.7 | 71.4 | 81.7 | | PP-YOLOE+_m | 80 | 49.8 | 67.1 | 54.5 | 31.8 | 53.9 | 66.2 | 53.3 | 75.0 | 84.6 | | PP-YOLOE+_l | 80 | 52.9 | 70.1 | 57.9 | 35.2 | 57.5 | 69.1 | 56.0 | 77.9 | 86.9 | | PP-YOLOE+_x | 80 | 54.7 | 72.0 | 59.9 | 37.9 | 59.3 | 70.4 | 57.0 | 78.7 | 87.2 | ### End-to-end Speed | Model | AP0.5:0.95 | TRT-FP32(fps) | TRT-FP16(fps) | |:-----------:|:---------------:|:-------------:|:-------------:| | PP-YOLOE+_s | 43.7 | 44.44 | 47.85 | | PP-YOLOE+_m | 49.8 | 39.06 | 43.86 | | PP-YOLOE+_l | 52.9 | 34.01 | 42.02 | | PP-YOLOE+_x | 54.7 | 26.88 | 36.76 | **Notes:** - PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset. - The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**. - PP-YOLOE used 8 GPUs for mixed precision training, if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault)**. - 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. - Refer to [Speed testing](#Speed-testing) to reproduce the speed testing results of PP-YOLOE. - If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`. - End-to-end speed test includes pre-processing + inference + post-processing and NMS time, using **Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz**, **single Tesla V100**, **CUDA 11.2**, **CUDNN 8.2.0**, **TensorRT 8.0.1.6**. ### Model Zoo on VOC | Model | Epoch | GPU number | images/GPU | backbone | input shape | Box AP0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config | |:---------------:|:-----:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :-------: |:--------:| | PP-YOLOE+_s | 30 | 8 | 8 | cspresnet-s | 640 | 86.7 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_s_30e_voc.yml) | | PP-YOLOE+_l | 30 | 8 | 8 | cspresnet-l | 640 | 89.0 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_l_30e_voc.yml) | ### Feature Models The PaddleDetection team provides configs and weights of various feature detection models based on PP-YOLOE, which users can download for use: |Scenarios | Related Datasets | Links| | :--------: | :---------: | :------: | |Pedestrian Detection | CrowdHuman | [pphuman](../pphuman) | |Vehicle Detection | BDD100K, UA-DETRAC | [ppvehicle](../ppvehicle) | |Small Object Detection | VisDrone、DOTA、xView | [smalldet](../smalldet) | ## Getting Start ### Datasets and Metrics PaddleDetection team provides **COCO and VOC dataset** , decompress and place it under `PaddleDetection/dataset/`: ``` wget https://bj.bcebos.com/v1/paddledet/data/coco.tar # tar -xvf coco.tar wget https://bj.bcebos.com/v1/paddledet/data/voc.zip # unzip voc.zip ``` **Note:** - For the format of COCO style dataset, please refer to [format-data](https://cocodataset.org/#format-data) and [format-results](https://cocodataset.org/#format-results). - For the evaluation metric of COCO, please refer to [detection-eval](https://cocodataset.org/#detection-eval), and install [cocoapi](https://github.com/cocodataset/cocoapi) at first. - For the evaluation metric of VOC, please refer to [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html). ### Custom dataset 1.For the annotation of custom dataset, please refer to [DetAnnoTools](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/DetAnnoTools_en.md); 2.For training preparation of custom dataset，please refer to [PrepareDataSet](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/PrepareDetDataSet_en.md). ### Training Training PP-YOLOE+ 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_plus_crn_l_80e_coco.yml --eval --amp ``` **Notes:** - If you need to evaluate while training, please add `--eval`. - PP-YOLOE+ supports mixed precision training, please add `--amp`. - PaddleDetection supports multi-machine distribued training, you can refer to [DistributedTraining tutorial](../../docs/DistributedTraining_en.md). ### 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_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_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`. ### 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_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_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_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams --infer_dir=demo ``` ### Exporting models For deployment on GPU or speed testing, 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 -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_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 -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=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 -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=True # install paddle2onnx pip install paddle2onnx # convert to onnx paddle2onnx --model_dir output_inference/ppyoloe_plus_crn_l_80e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_plus_crn_l_80e_coco.onnx ``` **Notes:** ONNX model only supports batch_size=1 now ### Speed testing For fair comparison, the speed in [Model Zoo](#Model-Zoo) 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. Thus, you should export model with extra `-o exclude_nms=True` setting. **Using Paddle Inference without TensorRT** to test speed, run following command ```bash # export inference model python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams exclude_nms=True # speed testing with run_benchmark=True CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True ``` **Using Paddle Inference with TensorRT** to test speed, run following command ```bash # export inference model with trt=True python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams exclude_nms=True trt=True # speed testing with run_benchmark=True,run_mode=trt_fp32/trt_fp16 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=trt_fp16 --device=gpu --run_benchmark=True ``` **Using TensorRT Inference with ONNX** to test speed, run following command ```bash # export inference model with trt=True python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams exclude_nms=True trt=True # convert to onnx paddle2onnx --model_dir output_inference/ppyoloe_plus_crn_s_80e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_crn_s_80e_coco.onnx # trt inference using fp16 and batch_size=1 trtexec --onnx=./ppyoloe_plus_crn_s_80e_coco.onnx --saveEngine=./ppyoloe_s_bs1.engine --workspace=1024 --avgRuns=1000 --shapes=image:1x3x640x640,scale_factor:1x2 --fp16 # trt inference using fp16 and batch_size=32 trtexec --onnx=./ppyoloe_plus_crn_s_80e_coco.onnx --saveEngine=./ppyoloe_s_bs32.engine --workspace=1024 --avgRuns=1000 --shapes=image:32x3x640x640,scale_factor:32x2 --fp16 # Using the above script, T4 and tensorrt 7.2 machine, the speed of PPYOLOE-s model is as follows, # batch_size=1, 2.80ms, 357fps # batch_size=32, 67.69ms, 472fps ``` ### Deployment PP-YOLOE can be deployed by following approches: - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp) - [Paddle-TensorRT](../../deploy/TENSOR_RT.md) - [PaddleServing](https://github.com/PaddlePaddle/Serving) - [PaddleSlim](../slim) Next, we will introduce how to use Paddle Inference to deploy PP-YOLOE models in TensorRT FP16 mode. First, refer to [Paddle Inference Docs](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python), download and install packages corresponding to CUDA, CUDNN and TensorRT version. Then, Exporting PP-YOLOE for Paddle Inference **with TensorRT**, use following command. ```bash python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=True ``` Finally, inference in TensorRT FP16 mode. ```bash # inference single image CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16 # inference all images in the directory CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu --run_mode=trt_fp16 ``` **Notes:** - TensorRT will perform optimization for the current hardware platform according to the definition of the network, generate an inference engine and serialize it into a file. This inference engine is only applicable to the current hardware hardware platform. If your hardware and software platform has not changed, you can set `use_static=True` in [enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660). In this way, the serialized file generated will be saved in the `output_inference` folder, and the saved serialized file will be loaded the next time when TensorRT is executed. - PaddleDetection release/2.4 and later versions will support NMS calling TensorRT, which requires PaddlePaddle release/2.3 and later versions. ### Other Datasets Model | AP | AP50 ---|---|--- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) | 22.6 | 37.5 [YOLOv5](https://github.com/ultralytics/yolov5) | 26.0 | 42.7 **PP-YOLOE** | **30.5** | **46.4** **Notes** - Here, we use [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) dataset, and to detect 9 objects including `person, bicycles, car, van, truck, tricyle, awning-tricyle, bus, motor`. - Above models trained using official default config, and load pretrained parameters on COCO dataset. - *Due to the limited time, more verification results will be supplemented in the future. You are also welcome to contribute to PP-YOLOE* ## Appendix Ablation experiments of PP-YOLOE. | NO. | Model | Box APval | 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 |