| Model |
Batch |
Scale |
APval
0.5:0.95
| APval
0.5
| FLOPs
(G)
| Params
(M)
| Weight |
| YOLOv8-N |
8xb16 |
640 |
37.0 |
52.9 |
8.8 |
3.2 |
ckpt |
| YOLOv8-S |
8xb16 |
640 |
|
|
|
|
|
| YOLOv8-M |
8xb16 |
640 |
|
|
|
|
|
| YOLOv8-L |
8xb16 |
640 |
50.7 |
68.3 |
165.7 |
43.7 |
ckpt |
- For training, we train YOLOv8 series with 500 epochs on COCO.
- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of YOLOv8.
- For optimizer, we use AdamW with weight decay 0.05 and base per image lr 0.001 / 64, which is different from the official YOLOv8. We have tried SGD, but it has weakened performance. For example, when using SGD, YOLOv8-N's AP was only 35.8%, lower than the current result (36.8 %), perhaps because some hyperparameters were not set properly.
- For learning rate scheduler, we use linear decay scheduler.
Train YOLOv8
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 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --multi_scale
Multi GPU
Taking training YOLOv8 on COCO as the example,
python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov8_s -bs 128 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/
Test YOLOv8
Taking testing YOLOv8 on COCO-val as the example,
python test.py --cuda -d coco --root path/to/coco -m yolov8_s --weight path/to/yolov8.pth -size 640 -vt 0.4 --show
Evaluate YOLOv8
Taking evaluating YOLOv8 on COCO-val as the example,
python eval.py --cuda -d coco-val --root path/to/coco -m yolov8_s --weight path/to/yolov8.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 -size 640 -vt 0.4 --show
Detect with Video
python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov8_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
Detect with Camera
python demo.py --mode camera --cuda -m yolov8_s --weight path/to/weight -size 640 -vt 0.4 --show --gif
