| Model |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| ckpt |
logs |
| YOLOv8-S |
8xb16 |
640 |
|
|
|
|
|
|
Train YOLO
Single GPU
Taking training YOLOv8-S on COCO as the example,
python train.py --cuda -d coco --root path/to/coco -m yolov8_s -bs 16 --fp16
Multi GPU
Taking training YOLO on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda --distributed -d coco --root /data/datasets/ -m yolov8_s -bs 128 --fp16 --sybn
Test YOLO
Taking testing YOLO on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolov8_s --weight path/to/yolo.pth --show
Evaluate YOLO
Taking evaluating YOLO on COCO-val as the example,
python eval.py --cuda -d coco --root path/to/coco -m yolov8_s --weight path/to/yolo.pth
Demo
Detect with Image
python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov8_s --weight path/to/weight --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov8_s --weight path/to/weight --show
Detect with Camera
python demo.py --mode camera --cuda -m yolov8_s --weight path/to/weight --show
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