ppyoloe

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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 for details

Table of Contents

• Introduction
• Model Zoo
• Getting Start
• 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.

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

PP-YOLOE is composed of following methods:

• Scalable backbone and neck
• Task Alignment Learning
• Efficient Task-aligned head with DFL and VFL
• SiLU(Swish) activation function

Model Zoo

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	config
PP-YOLOE+_m	80	8	8	cspresnet-m	640	49.8	50.0	23.43	49.91	123.4	208.3	model	config
PP-YOLOE+_l	80	8	8	cspresnet-l	640	52.9	53.3	52.20	110.07	78.1	149.2	model	config
PP-YOLOE+_x	80	8	8	cspresnet-x	640	54.7	54.9	98.42	206.59	45.0	95.2	model	config

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

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 lr_new = lr_default * (batch_size_new * GPU_number_new) / (batch_size_default * GPU_number_default).
• 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 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.

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
Vehicle Detection	BDD100K, UA-DETRAC	ppvehicle
Small Object Detection	VisDrone	visdrone

Getting Start

Training

Training PP-YOLOE+ 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_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.

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_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 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.

# 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

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.

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.

# 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

# 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 do not contains the time cost of data reading and post-processing(NMS), which is same as YOLOv4(AlexyAB) 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

# 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

# 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

# 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 & C++
• Paddle-TensorRT
• PaddleServing
• PaddleSlim

Next, we will introduce how to use Paddle Inference to deploy PP-YOLOE models in TensorRT FP16 mode.

First, refer to Paddle Inference Docs, download and install packages corresponding to CUDA, CUDNN and TensorRT version.

Then, Exporting PP-YOLOE for Paddle Inference with TensorRT, use following command.

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.

# 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. 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	22.6	37.5
YOLOv5	26.0	42.7
PP-YOLOE	30.5	46.4

Notes

• 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