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YOLOF: You Only Look One-level Feature

Our YOLOF-R50-1x baseline on COCO-val:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.380
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.577
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.405
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.199
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.315
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.513
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.555
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.333
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.736

ImageNet-1K_V1 pretrained

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

Model	scale	AP^val 0.5:0.95	AP^val 0.5	Weight	Logs
YOLOF_R18_C5_1x	800,1333	32.8	51.2	ckpt	log
YOLOF_R50_C5_1x	800,1333	38.0	57.7	ckpt	log
YOLOF_R50_DC5_1x	800,1333	39.5	58.5	ckpt	log

README.md