YOLOv5:
| Model |
Backbone |
Batch |
Scale |
APval
0.5:0.95
| APval
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 |
- 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.
