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

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 Zoo on COCO

Model	Epoch	GPU number	images/GPU	backbone	input shape	Box AP^val 0.5:0.95	Box AP^{test 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+_m(distill)	80	8	8	cspresnet-m	640	51.0	51.2	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+_l(distill)	80	8	8	cspresnet-l	640	54.0	54.4	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

Note:

M and L models use distillation, please refer to distill for details.

Tiny model

Model	Epoch	GPU number	images/GPU	backbone	input shape	Box AP^val 0.5:0.95	Box AP^{test 0.5:0.95}	Params(M)	FLOPs(G)	T4 TensorRT FP16(FPS)	download	config
PP-YOLOE+_t-aux(640)	300	8	8	cspresnet-t	640	39.9	56.6	4.85	19.15	344.8	model	config
PP-YOLOE+_t-aux(640)-relu	300	8	8	cspresnet-t	640	36.4	53.0	3.60	12.17	476.2	model	config
PP-YOLOE+_t-aux(320)	300	8	8	cspresnet-t	320	33.3	48.5	4.85	4.80	729.9	model	config
PP-YOLOE+_t-aux(320)-relu	300	8	8	cspresnet-t	320	30.1	44.7	3.60	3.04	984.8	model	config

Comprehensive Metrics

Model	Epoch	AP^0.5:0.95	AP^0.5	AP^0.75	AP^small	AP^medium	AP^large	AR^small	AR^medium	AR^large
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+_m(distill)	80	51.0	68.1	55.8	32.5	55.7	67.4	51.9	76.1	86.4
PP-YOLOE+_l	80	52.9	70.1	57.9	35.2	57.5	69.1	56.0	77.9	86.9
PP-YOLOE+_l(distill)	80	54.0	71.2	59.2	36.1	58.8	70.4	55.0	78.7	87.7
PP-YOLOE+_x	80	54.7	72.0	59.9	37.9	59.3	70.4	57.0	78.7	87.2

Note:

M and L models use distillation, please refer to distill for details.

End-to-end Speed

Model	AP^0.5:0.95	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 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.
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 Objects365

Model	Epoch	Machine number	GPU number	images/GPU	backbone	input shape	Box AP^0.5	Params(M)	FLOPs(G)	V100 FP32(FPS)	V100 TensorRT FP16(FPS)	download	config
PP-YOLOE+_s	60	3	8	8	cspresnet-s	640	18.1	7.93	17.36	208.3	333.3	model	config
PP-YOLOE+_m	60	4	8	8	cspresnet-m	640	25.0	23.43	49.91	123.4	208.3	model	config
PP-YOLOE+_l	60	3	8	8	cspresnet-l	640	30.8	52.20	110.07	78.1	149.2	model	config
PP-YOLOE+_x	60	4	8	8	cspresnet-x	640	32.7	98.42	206.59	45.0	95.2	model	config

Notes:

The Details for multiple machine and multi-gpu training, see DistributedTraining
For Objects365 dataset download, please refer to objects365 official website. The specific category list can be downloaded from objects365_detection_label_list.txt organized by PaddleDetection team. It should be stored in dataset/objects365/, and each line represents one category. The categories need to be read when exporting the model or doing inference. If the json file is not exist, you can make the following changes to configs/datasets/objects365_detection.yml:

TestDataset:
  !ImageFolder
    # anno_path: annotations/zhiyuan_objv2_val.json
    anno_path: objects365_detection_label_list.txt
    dataset_dir: dataset/objects365/

Model Zoo on VOC

Model	Epoch	GPU number	images/GPU	backbone	input shape	Box AP^0.5	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	config
PP-YOLOE+_l	30	8	8	cspresnet-l	640	89.0	52.20	110.07	78.1	149.2	model	config

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、DOTA、xView	smalldet
Densely Packed Object Detection	SKU110k	application
Rotated Object Detection	DOTA	PP-YOLOE-R

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 and format-results.
For the evaluation metric of COCO, please refer to detection-eval, and install cocoapi at first.
For the evaluation metric of VOC, please refer to VOC2012.

Custom dataset

1.For the annotation of custom dataset, please refer to DetAnnoTools;

2.For training preparation of custom dataset，please refer to PrepareDataSet.

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 distributed 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 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 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 approaches:

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, tricycle, awning-tricycle, 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 AP^val	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

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README.md

README.md

PP-YOLOE

Latest News

Legacy model

Table of Contents

Introduction

Model Zoo

Model Zoo on COCO

Tiny model

Comprehensive Metrics

End-to-end Speed

Model Zoo on Objects365

Model Zoo on VOC

Feature Models

Getting Start

Datasets and Metrics

Custom dataset

Training

Evaluation

Inference

Exporting models

Speed testing

Deployment

Other Datasets

Appendix

Files

README.md

Latest commit

History

README.md

File metadata and controls

PP-YOLOE

Latest News

Legacy model

Table of Contents

Introduction

Model Zoo

Model Zoo on COCO

Tiny model

Comprehensive Metrics

End-to-end Speed

Model Zoo on Objects365

Model Zoo on VOC

Feature Models

Getting Start

Datasets and Metrics

Custom dataset

Training

Evaluation

Inference

Exporting models

Speed testing

Deployment

Other Datasets

Appendix