Skip to content

Latest commit

 

History

History
181 lines (154 loc) · 7.53 KB

README.md

File metadata and controls

181 lines (154 loc) · 7.53 KB
Raw

YOLOv9 for Fracture Detection

YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images

PWC

Comparison

Performance

Model Test Size Param. FLOPs F1 Score AP50val AP50-95val Speed
YOLOv8 640 43.61M 164.9G 0.59 62.44% 40.32% 3.6ms
YOLOv8+SA 640 43.64M 165.4G 0.62 63.99% 41.49% 3.9ms
YOLOv8+ECA 640 43.64M 165.5G 0.61 62.64% 40.21% 3.6ms
YOLOv8+GAM 640 49.29M 183.5G 0.60 63.32% 40.74% 8.7ms
YOLOv8+ResGAM 640 49.29M 183.5G 0.62 63.97% 41.18% 9.4ms
YOLOv8+ResCBAM 640 53.87M 196.2G 0.62 62.95% 40.10% 4.1ms
YOLOv9-C 640 51.02M 239.0G 0.64 65.31% 42.66% 5.2ms
YOLOv9-E 640 69.42M 244.9G 0.64 65.46% 43.32% 6.4ms

Citation

If you find our paper useful in your research, please consider citing:

@article{chien2024yolov9,
  title={YOLOv9 for fracture detection in pediatric wrist trauma X-ray images},
  author={Chien, Chun-Tse and Ju, Rui-Yang and Chou, Kuang-Yi and Chiang, Jen-Shiun},
  journal={Electronics Letters},
  volume={60},
  number={11},
  pages={e13248},
  year={2024},
  publisher={Wiley Online Library}
}

Requirements

  • Linux (Ubuntu)
  • Python = 3.9
  • Pytorch = 1.13.1
  • NVIDIA GPU + CUDA CuDNN

Environment

  pip install -r requirements.txt

Overall Flowchart

Dataset Split

  • GRAZPEDWRI-DX Dataset (Download Link)

  • Download dataset and put images and annotatation into ./GRAZPEDWRI-DX_dataset/data/images, ./GRAZPEDWRI-DX_dataset/data/labels.

      python split.py

  • The dataset is divided into training, validation, and testing set (70-20-10 %) according to the key patient_id stored in dataset.csv.

  • The script then will move the files into the relative folder as it is represented here below.

     GRAZPEDWRI-DX_dataset
    └── data   
         ├── images
         │    ├── train
         │    │    ├── train_img1.png
         │    │    └── ...
         │    ├── valid
         │    │    ├── valid_img1.png
         │    │    └── ...
         │    └── test
         │         ├── test_img1.png
         │         └── ...
         └── labels
              ├── train
              │    ├── train_annotation1.txt
              │    └── ...
              ├── valid
              │    ├── valid_annotation1.txt
              │    └── ...
              └── test
                   ├── test_annotation1.txt
                   └── ...

The script will create 3 files: train_data.csv, valid_data.csv, and test_data.csv with the same structure of dataset.csv.

Data Augmentation

  • Data augmentation of the training set using the addWeighted function doubles the size of the training set.
  python imgaug.py --input_img /path/to/input/train/ --output_img /path/to/output/train/ --input_label /path/to/input/labels/ --output_label /path/to/output/labels/

For example:

  python imgaug.py --input_img ./GRAZPEDWRI-DX/data/images/train/ --output_img ./GRAZPEDWRI-DX/data/images/train_aug/ --input_label ./GRAZPEDWRI-DX/data/labels/train/ --output_label ./GRAZPEDWRI-DX/data/labels/train_aug/

  • The path of the processed file is shown below:

     GRAZPEDWRI-DX_dataset
    └── data   
         ├── images
         │    ├── train
         │    │    ├── train_img1.png
         │    │    └── ...
         │    ├── train_aug
         │    │    ├── train_aug_img1.png
         │    │    └── ...
         │    ├── valid
         │    │    ├── valid_img1.png
         │    │    └── ...
         │    └── test
         │         ├── test_img1.png
         │         └── ...
         └── labels
              ├── train
              │    ├── train_annotation1.txt
              │    └── ...
              ├── train_aug
              │    ├── train_aug_annotation1.txt
              │    └── ...
              ├── valid
              │    ├── valid_annotation1.txt
              │    └── ...
              └── test
                   ├── test_annotation1.txt
                   └── ...

Weights

If you plan to use pretrained models to train, you need put them into ./weights/.

  • You can get the YOLOv9 pretained models on the MS COCO 2017 Dataset through YOLOv9 official GitHub.
  • Use gdown to download the trained model from our GitHub:
  gdown https://github.com/RuiyangJu/YOLOv9-Fracture-Detection/releases/download/Trained/weights.zip

Train & Validate

Before training the model, make sure the path to the data in the ./data/meta.yaml file is correct.

  • meta.yaml
  # patch: /path/to/GRAZPEDWRI-DX/data
  path: 'E:/GRAZPEDWRI-DX/data'
  train: 'images/train_aug'
  val: 'images/valid'
  test: 'images/test'
  • Arguments
Key Value Description
workers 8 number of worker threads for data loading (per RANK if DDP)
device None device to run on, i.e. device=0,1,2,3 or device=cpu
model None path to model file, i.e. yolov8n.pt, yolov8n.yaml
batch 16 number of images per batch (-1 for AutoBatch)
data None path to data file, i.e. coco128.yaml
img 640 size of input images as integer, i.e. 640, 1024
cfg yolo.yaml path to model.yaml, i.e. yolov9-c.yaml
weights None initial weights path
name exp save to project/name
hyp data/hyps/hyp.scratch-high.yaml hyperparameters path
epochs 100 number of epochs to train for
  • Example
  python train_dual.py --workers 8 --device 0 --batch 16 --data data/meta.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights weights/yolov9-c.pt --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 100 --close-mosaic 15

Related Works

Expand