| Model |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
Logs |
| YOLOv3-S |
1xb16 |
640 |
31.3 |
49.2 |
25.2 |
7.3 |
ckpt |
log |
- For training, we train redesigned YOLOv3 with 300 epochs on COCO. We also use the gradient accumulation.
- For data augmentation, we use the RandomAffine, RandomHSV, Mosaic and Mixup augmentation.
- For optimizer, we use AdamW with weight decay of 0.05 and per image base lr of 0.001 / 64.
- For learning rate scheduler, we use cosine decay scheduler.
- For batch size, we set it to 16, and we also use the gradient accumulation to approximate batch size of 256.
Train YOLOv3
Single GPU
Taking training YOLOv3-S on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m yolov3_s -bs 16 --fp16
Multi GPU
Taking training YOLOv3-S on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda --distributed -d coco --root path/to/coco -m yolov3_s -bs 16 --fp16
Test YOLOv3
Taking testing YOLOv3-S on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolov3_s --weight path/to/yolov3.pth --show
Evaluate YOLOv3
Taking evaluating YOLOv3-S on COCO-val as the example,
python eval.py --cuda -d coco --root path/to/coco -m yolov3_s --weight path/to/yolov3.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov3_s --weight path/to/weight --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov3_s --weight path/to/weight --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolov3_s --weight path/to/weight --show --gif
