YOLO-Tutorial-v2/odlab/models/detectors/yolof

YOLOF: You Only Look One-level Feature

• COCO

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

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Model	scale	FPSFP32 RTX 4060	APval 0.5:0.95	APval 0.5	Weight	Logs
YOLOF_R18_C5_1x	800,1333	54	32.8	51.4	ckpt	log
YOLOF_R50_C5_1x	800,1333	21	37.7	57.2	ckpt	log