YOLOv5:

For training, we train YOLOv5 series with 300 epochs on COCO.
For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of YOLOv5.
For optimizer, we use SGD with momentum 0.937, weight decay 0.0005 and base lr 0.01.
For learning rate scheduler, we use linear decay scheduler.
For YOLOv5's structure, we use decoupled head, following the setting of YOLOX.
For YOLOv5-M and YOLOv5-L, increasing the batch size may improve performance. Due to my computing resources, I can only set the batch size to 16.

Model	Backbone	Batch	Scale	AP^val 0.5:0.95	AP^val 0.5	FLOPs ^(G)	Params ^(M)	Weight
YOLOv5-N	CSPDarkNet-N	1xb16	640	29.8	47.1	7.7	2.4	ckpt
YOLOv5-S	CSPDarkNet-S	1xb16	640	37.8	56.5	27.1	9.0	ckpt
YOLOv5-M	CSPDarkNet-M	1xb16	640	43.5	62.5	74.3	25.4	ckpt
YOLOv5-L	CSPDarkNet-L	1xb16	640	46.7	65.5	155.6	54.2	ckpt