This is a repository for implementation of YOLOv8 for easy customization and understanding underlying techniques in it, which is refered to ultralytics' YOLOv8 (https://github.com/ultralytics/ultralytics).
You can train your own YOLOv8 model with command like below. As for you can refer sample file in cfg/.yaml, and make .yaml file following your dataset. Since cfg/.json file that is required to compute mAP scores is built automatically via dataloader, you do not have to worry about it.
-
Pretrained Model Weights Download
| Model | Dataset | Train | Valid | Size (pixel) |
mAP (@0.5:0.95) |
Params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|---|
| YOLOv8n | COCO | train2017 | val2017 | 640 | 37.3 | 3.2 | 8.7 |
| YOLOv8s | COCO | train2017 | val2017 | 640 | 44.9 | 11.2 | 28.6 |
| YOLOv8m | COCO | train2017 | val2017 | 640 | 50.2 | 25.9 | 78.9 |
| YOLOv8l | COCO | train2017 | val2017 | 640 | 52.9 | 43.7 | 165.2 |
| YOLOv8x | COCO | train2017 | val2017 | 640 | 53.9 | 68.2 | 257.8 |
# Training
python train.py --arch yolov8n --img-size 640 --num-epochs 200 --mosaic --close-mosaic 5 --model-ema --project <YOUR PROJECT> --dataset <YOUR DATASET>
# Evaluation
python val.py --project <YOUR PROJECT>
# Inference in images
python test.py --project <YOUR PROJECT> --test-dir <IMAGE DIRECTORY>
# Inference in video
python infer.py --project <YOUR PROJECT> --vid_path <VIDEO PATH>- Author: Jiho Park
- Email: [email protected]