YOLOv11 re-implementation using PyTorch
conda create -n YOLO python=3.10.10
conda activate YOLO
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
pip install opencv-python
pip install PyYAML
pip install tqdm
- Configure your dataset path in
main.pyfor training - Run
bash main.sh $ --train --train-dir <path_to_train_dir> --val-dir <path_to_val_dir> --test-dir <path_to_test_dir>for training,$is number of GPUs
- Configure your dataset path in
main.pyfor testing - Run
python main.py --test --train-dir <path_to_train_dir> --val-dir <path_to_val_dir> --test-dir <path_to_test_dir>for testing
| Version | Epochs | Box mAP | Download |
|---|---|---|---|
| v11_n | 600 | 38.6 | Model |
| v11_n* | - | 39.2 | Model |
| v11_s* | - | 46.5 | Model |
| v11_m* | - | 51.2 | Model |
| v11_l* | - | 53.0 | Model |
| v11_x* | - | 54.3 | Model |
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.415
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.569
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.321
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.533
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.588
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.777
*means that it is from original repository, see reference- In the official YOLOv11 code, mask annotation information is used, which leads to higher performance
├── COCO
├── images
├── train2017
├── 1111.jpg
├── 2222.jpg
├── val2017
├── 1111.jpg
├── 2222.jpg
├── labels
├── train2017
├── 1111.txt
├── 2222.txt
├── val2017
├── 1111.txt
├── 2222.txt