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ppyoloe

README.md

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PP-YOLOE

Table of Contents

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.

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

PP-YOLOE is composed of following methods:

Model Zoo

Model GPU number images/GPU backbone input shape Box APval Box APtest Params(M) FLOPs(G) V100 FP32(FPS) V100 TensorRT FP16(FPS) download config
PP-YOLOE-s 8 32 cspresnet-s 640 42.7 43.1 7.93 17.36 208.3 333.3 model config
PP-YOLOE-m 8 28 cspresnet-m 640 48.6 48.9 23.43 49.91 123.4 208.3 model config
PP-YOLOE-l 8 20 cspresnet-l 640 50.9 51.4 52.20 110.07 78.1 149.2 model config
PP-YOLOE-x 8 16 cspresnet-x 640 51.9 52.2 98.42 206.59 45.0 95.2 model config

Notes:

  • PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，Box APtest 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.
  • 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) 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

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:

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 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.

# 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.

python tools/export_model.py -c 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.

python tools/export_model.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams trt=True

deploy/python/infer.py is used to load exported paddle inference model above for inference and benchmark through Paddle Inference.

# 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.

# 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

5. Other Datasets

Model AP AP50
YOLOX 22.6 37.5
YOLOv5 26.0 42.7
PP-YOLOE 30.5 46.4

Note

  • Here, we use VisDrone 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