YOLOv4:
| Model |
Backbone |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOv4-Tiny |
CSPDarkNet-Tiny |
1xb16 |
640 |
31.0 |
49.1 |
8.1 |
2.9 |
ckpt |
| YOLOv4 |
CSPDarkNet-53 |
1xb16 |
640 |
46.6 |
65.8 |
162.7 |
61.5 |
ckpt |
- For training, we train YOLOv4 and YOLOv4-Tiny with 250 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 YOLOv4's structure, we use decoupled head, following the setting of YOLOX.
