yjh0410 264112178f build a new YOLO-Tutorial project for my book		il y a 1 an
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README.md	264112178f build a new YOLO-Tutorial project for my book	il y a 1 an
build.py	264112178f build a new YOLO-Tutorial project for my book	il y a 1 an
criterion.py	264112178f build a new YOLO-Tutorial project for my book	il y a 1 an
matcher.py	264112178f build a new YOLO-Tutorial project for my book	il y a 1 an
retinanet.py	264112178f build a new YOLO-Tutorial project for my book	il y a 1 an

RetinaNet

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

ImageNet-1K_V1 pretrained

Train RetinaNet

Single GPU

Taking training RetinaNet_R18_1x on COCO as the example,

python main.py --cuda -d coco --root path/to/coco -m retinanet_r18_1x --batch_size 16 --eval_epoch 2

Multi GPU

Taking training RetinaNet_R18_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 retinanet_r18_1x --batch_size 16 --eval_epoch 2

Test RetinaNet

Taking testing RetinaNet_R18_1x on COCO-val as the example,

python test.py --cuda -d coco --root path/to/coco -m retinanet_r18_1x --weight path/to/retinanet_r18_1x.pth -vt 0.4 --show

Evaluate RetinaNet

Taking evaluating RetinaNet_R18_1x on COCO-val as the example,

python main.py --cuda -d coco --root path/to/coco -m retinanet_r18_1x --resume path/to/retinanet_r18_1x.pth --eval_first

Demo

Detect with Image

python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m retinanet_r18_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 retinanet_r18_1x --weight path/to/weight -vt 0.4 --show --gif

Detect with Camera

python demo.py --mode camera --cuda -m retinanet_r18_1x --weight path/to/weight -vt 0.4 --show --gif

Model	scale	FPS	AP^val 0.5:0.95	AP^val 0.5	Weight	Logs
RetinaNet_R18_1x	800,1333		30.5	48.1	ckpt	log
RetinaNet_R50_1x	800,1333

README.md