80 lines
3.4 KiB
Markdown
80 lines
3.4 KiB
Markdown
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YOLOv11 re-implementation using PyTorch
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### fix
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* fix the label size [0,1] tensor, which have two dim not adjusted size of [1,]
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* if pic do not have object (if label is empty), the phenomenon occurs
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* find XXX to look
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### Installation
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```
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conda create -n YOLO python=3.10.10
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conda activate YOLO
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conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
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pip install opencv-python
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pip install PyYAML
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pip install tqdm
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```
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### Train
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* Configure your dataset path in `main.py` for training
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* Run `bash main.sh $ --train` for training, `$` is number of GPUs
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* Run `nohup bash main.sh 1 --train --epochs 300 > train.log 2>&1 &` for training in background
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### Test
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* Configure your dataset path in `main.py` for testing
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* Run `python main.py --test` for testing
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### Results
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| Version | Epochs | Box mAP | Download |
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|:-------:|:------:|--------:|--------------------------------------------------------------------------------------:|
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| v11_n | 600 | 38.6 | [Model](https://github.com/jahongir7174/YOLOv11-pt/blob/master/weights/best.pt) |
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| v11_n* | - | 39.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_n.pt) |
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| v11_s* | - | 46.5 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_s.pt) |
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| v11_m* | - | 51.2 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_m.pt) |
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| v11_l* | - | 53.0 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_l.pt) |
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| v11_x* | - | 54.3 | [Model](https://github.com/jahongir7174/YOLOv11-pt/releases/download/v0.0.1/v11_x.pt) |
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```
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.551
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.415
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.569
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.321
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.533
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.588
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.777
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```
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* `*` means that it is from original repository, see reference
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* In the official YOLOv11 code, mask annotation information is used, which leads to higher performance
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### Dataset structure
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├── COCO
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├── images
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├── train2017
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├── 1111.jpg
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├── 2222.jpg
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├── val2017
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├── 1111.jpg
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├── 2222.jpg
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├── labels
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├── train2017
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├── 1111.txt
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├── 2222.txt
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├── val2017
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├── 1111.txt
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├── 2222.txt
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#### Reference
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* [YOLOv11-pt](https://github.com/jahongir7174/YOLOv11-pt)
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