| Model |
scale |
FPS |
APval 0.5:0.95
| APval 0.5
| Weight |
Logs |
| YOLOF_R18_C5_1x |
800,1333 |
|
32.8 |
51.4 |
ckpt |
log |
| YOLOF_R50_C5_1x |
800,1333 |
|
|
|
|
|
Train YOLOF
Single GPU
Taking training YOLOF_R18_C5_1x on COCO as the example,
python main.py --cuda -d coco --root path/to/coco -m yolof_r18_c5_1x --batch_size 16 --eval_epoch 2
Multi GPU
Taking training YOLOF_R18_C5_1x on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root path/to/coco -m yolof_r18_c5_1x --batch_size 16 --eval_epoch 2
Test YOLOF
Taking testing YOLOF_R18_C5_1x on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolof_r18_c5_1x --weight path/to/yolof_r18_c5_1x.pth -vt 0.4 --show
Evaluate YOLOF
Taking evaluating YOLOF_R18_C5_1x on COCO-val as the example,
python main.py --cuda -d coco --root path/to/coco -m yolof_r18_c5_1x --resume path/to/yolof_r18_c5_1x.pth --eval_first
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolof_r18_c5_1x --weight path/to/weight -vt 0.4 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolof_r18_c5_1x --weight path/to/weight -vt 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolof_r18_c5_1x --weight path/to/weight -vt 0.4 --show --gif