MindYOLO Model Zoo and Baselines

Name	Scale	Context	ImageSize	Dataset	Box mAP (%)	Params	FLOPs	Recipe	Download
YOLOv8	N	D910x8-G	640	MS COCO 2017	37.2	3.2M	8.7G	yaml	weights
YOLOv8	S	D910x8-G	640	MS COCO 2017	44.6	11.2M	28.6G	yaml	weights
YOLOv8	M	D910x8-G	640	MS COCO 2017	50.5	25.9M	78.9G	yaml	weights
YOLOv8	L	D910x8-G	640	MS COCO 2017	52.8	43.7M	165.2G	yaml	weights
YOLOv8	X	D910x8-G	640	MS COCO 2017	53.7	68.2M	257.8G	yaml	weights
YOLOv7	Tiny	D910x8-G	640	MS COCO 2017	37.5	6.2M	13.8G	yaml	weights
YOLOv7	L	D910x8-G	640	MS COCO 2017	50.8	36.9M	104.7G	yaml	weights
YOLOv7	X	D910x8-G	640	MS COCO 2017	52.4	71.3M	189.9G	yaml	weights
YOLOv5	N	D910x8-G	640	MS COCO 2017	27.3	1.9M	4.5G	yaml	weights
YOLOv5	S	D910x8-G	640	MS COCO 2017	37.6	7.2M	16.5G	yaml	weights
YOLOv5	M	D910x8-G	640	MS COCO 2017	44.9	21.2M	49.0G	yaml	weights
YOLOv5	L	D910x8-G	640	MS COCO 2017	48.5	46.5M	109.1G	yaml	weights
YOLOv5	X	D910x8-G	640	MS COCO 2017	50.5	86.7M	205.7G	yaml	weights
YOLOv4	CSPDarknet53	D910x8-G	608	MS COCO 2017	45.4	27.6M	52G	yaml	weights
YOLOv4	CSPDarknet53(silu)	D910x8-G	608	MS COCO 2017	45.8	27.6M	52G	yaml	weights
YOLOv3	Darknet53	D910x8-G	640	MS COCO 2017	45.5	61.9M	156.4G	yaml	weights
YOLOX	N	D910x8-G	416	MS COCO 2017	24.1	0.9M	1.1G	yaml	weights
YOLOX	Tiny	D910x8-G	416	MS COCO 2017	33.3	5.1M	6.5G	yaml	weights
YOLOX	S	D910x8-G	640	MS COCO 2017	40.7	9.0M	26.8G	yaml	weights
YOLOX	M	D910x8-G	640	MS COCO 2017	46.7	25.3M	73.8G	yaml	weights
YOLOX	L	D910x8-G	640	MS COCO 2017	49.2	54.2M	155.6G	yaml	weights
YOLOX	X	D910x8-G	640	MS COCO 2017	51.6	99.1M	281.9G	yaml	weights
YOLOX	Darknet53	D910x8-G	640	MS COCO 2017	47.7	63.7M	185.3G	yaml	weights

Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
Box mAP: Accuracy reported on the validation set.

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