This is a repository for implementation of YOLOv5 for easy customization and understanding underlying techniques in it, which is refered to ultralytics' YOLOv5 (https://github.com/ultralytics/yolov5).
You can train your own YOLOv5 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.
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Pretrained Model Weights Download
| Model | Dataset | Train | Valid | Size (pixel) |
mAP (@0.5:0.95) |
mAP (@0.5) |
Params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|---|---|
| YOLOv5n | COCO | train2017 | val2017 | 640 | 28.0 | 45.7 | 1.9 | 4.5 |
| YOLOv5s | COCO | train2017 | val2017 | 640 | 37.4 | 56.8 | 7.2 | 16.5 |
| YOLOv5m | COCO | train2017 | val2017 | 640 | 45.4 | 64.1 | 21.2 | 49.0 |
| YOLOv5l | COCO | train2017 | val2017 | 640 | 49.0 | 67.3 | 46.5 | 109.1 |
| YOLOv5x | COCO | train2017 | val2017 | 640 | 50.7 | 68.9 | 86.7 | 205.7 |
# Training
python train.py --arch yolov5n --img-size 640 --num-epochs 200 --mosaic --cos-lr --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: pjh5672.dev@gmail.com