PP-YOLOE Legacy Model Zoo (2022.03)

Legacy Model Zoo

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	400	8	32	cspresnet-s	640	43.4	43.6	7.93	17.36	208.3	333.3	model	config
PP-YOLOE-s	300	8	32	cspresnet-s	640	43.0	43.2	7.93	17.36	208.3	333.3	model	config
PP-YOLOE-m	300	8	28	cspresnet-m	640	49.0	49.1	23.43	49.91	123.4	208.3	model	config
PP-YOLOE-l	300	8	20	cspresnet-l	640	51.4	51.6	52.20	110.07	78.1	149.2	model	config
PP-YOLOE-x	300	8	16	cspresnet-x	640	52.3	52.4	98.42	206.59	45.0	95.2	model	config

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	download	config
PP-YOLOE-s	400	43.4	60.0	47.5	25.7	47.8	59.2	43.9	70.8	81.9	model	config
PP-YOLOE-s	300	43.0	59.6	47.2	26.0	47.4	58.7	45.1	70.6	81.4	model	config
PP-YOLOE-m	300	49.0	65.9	53.8	30.9	53.5	65.3	50.9	74.4	84.7	model	config
PP-YOLOE-l	300	51.4	68.6	56.2	34.8	56.1	68.0	53.1	76.8	85.6	model	config
PP-YOLOE-x	300	52.3	69.5	56.8	35.1	57.0	68.6	55.5	76.9	85.7	model	config

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 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.

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