| Model |
Backbone |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOv3-Tiny |
DarkNet-Tiny |
1xb16 |
640 |
25.4 |
43.4 |
7.0 |
2.3 |
ckpt |
| YOLOv3 |
DarkNet-53 |
1xb16 |
640 |
42.9 |
63.5 |
167.4 |
54.9 |
ckpt |
- For training, we train YOLOv3 and YOLOv3-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 YOLOv3's structure, we use decoupled head, following the setting of YOLOX.
Train YOLOv3
Single GPU
Taking training YOLOv3 on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m yolov3 -bs 16 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --multi_scale
Multi GPU
Taking training YOLOv3 on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov3 -bs 128 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/
Test YOLOv3
Taking testing YOLOv3 on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolov3 --weight path/to/yolov3_coco.pth -size 640 --show
Evaluate YOLOv3
Taking evaluating YOLOv3 on COCO-val as the example,
python eval.py --cuda -d coco --root path/to/coco -m yolov3 --weight path/to/yolov3_coco.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov3 --weight path/to/yolov3_coco.pth -size 640 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov3 --weight path/to/yolov3_coco.pth -size 640 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolov3 --weight path/to/yolov3_coco.pth -size 640 --show --gif
