YOLOv5:
| Model |
Backbone |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOX-N |
CSPDarkNet-N |
8xb8 |
640 |
30.4 |
48.9 |
7.5 |
2.3 |
ckpt |
| YOLOX-S |
CSPDarkNet-S |
8xb8 |
640 |
39.0 |
58.8 |
26.8 |
8.9 |
ckpt |
| YOLOX-M |
CSPDarkNet-M |
1xb16 |
640 |
44.6 |
63.8 |
74.3 |
25.4 |
ckpt |
| YOLOX-L |
CSPDarkNet-L |
1xb16 |
640 |
46.9 |
65.9 |
155.4 |
54.2 |
ckpt |
- For training, we train YOLOX 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 YOLOX.
- For optimizer, we use AdamW with weight decay 0.05 and base per image lr 0.001 / 64.
- For learning rate scheduler, we use linear decay scheduler.
- I am trying to retrain YOLOX-M and YOLOX-L with more GPUs, and I will update the AP of YOLOX-M and YOLOX-L in the table in the future.
