UPDATE PRETRAINED

Model	size (pixels)	dataset	mAPval 50-95	mAPval 50	Speed PyTorch RTX3090 (ms)	params (M)	FLOPs @640 (B)
YOLOv5x	640	COCO	48.1	66.7	19.7	86.71	205.5
YOLOv5l	640	COCO	46.2	65.2	11.7	46.53	109.0
YOLOv5m	640	COCO	42.9	62.1	5.0	21.17	48.9
YOLOv5s	640	COCO	34.7	53.8	3.6	7.23	16.4
YOLOv5n	640	COCO	24.4	41.3	3.5	1.87	4.5
YOLOv3	640	COCO	43.6	63.7	8.0	61.92	155.9
YOLOv5s	640	VOC	46.8	73.8	2.3	7.06	15.9
YOLOv3	640	VOC	56.9	81.9	7.1	61.60	154.9
YOLOv3-Tiny	640	VOC	25.3	54.2	1.9	8.71	13.0

Model	size (pixels)	acc top1	acc top5	Training 90 epochs 4xRTX3090 (hours)	Speed PyTorch RTX3090 (ms)	params (M)	FLOPs @224 (B)
YOLOv5s-cls	224	64.9	86.0	38.831	0.3	6.45	11.4
YOLOv3-cls	224	68.3	88.2	56.517	1.0	16.81	98.6
ResNet50	224	69.3	88.2	94.422	0.4	25.6	8.5
EfficientNet_b0	224	71.0	90.2	77.515	0.5	5.3	1.0

a Tiny Version of YOLOv5

1. Support training/evaluation/prediction/deployment of YOLOv5 detection model;
2. Support configuration files for the original YOLOv5 project, including data/*.yaml, hyps/*.yaml, models/*.yaml;
3. Provided a pre training model for YOLOv5n/YOLOv5s/YOLOv3 based on the coco dataset;
4. Provided a pre training model for YOLOv5s/YOLOv3/YOLOv3-Tiny based on VOC dataset.

Model	size (pixels)	dataset	mAPval 50-95	mAPval 50	Speed PyTorch RTX3090 (ms)	params (M)	FLOPs @640 (B)
YOLOv5n	640	COCO	24.4	41.3	3.5	1.87	4.5
YOLOv5s	640	COCO	34.7	53.8	3.6	7.23	16.4
YOLOv3	640	COCO	43.6	63.7	8.0	61.92	155.9

Model	size (pixels)	dataset	mAPval 50-95	mAPval 50	Speed PyTorch RTX3090 (ms)	params (M)	FLOPs @640 (B)
YOLOv5s	640	VOC	46.8	73.8	2.3	7.06	15.9
YOLOv3	640	VOC	56.9	81.9	7.1	61.60	154.9
YOLOv3-Tiny	640	VOC	25.3	54.2	1.9	8.71	13.0