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Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection

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YOLOv8+GC for Fracture Detection

Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection

PWC

Comparison

Performance

Model Test Size Param. FLOPs F1 Score AP50val Speed
YOLOv8 1024 43.61M 164.9G 0.62 63.58% 7.7ms
YOLOv8+SA 1024 43.64M 165.4G 0.63 64.25% 8.0ms
YOLOv8+ECA 1024 43.64M 165.5G 0.65 64.24% 7.7ms
YOLOv8+GAM 1024 49.29M 183.5G 0.65 64.26% 12.7ms
YOLOv8+ResGAM 1024 49.29M 183.5G 0.64 64.98% 18.1ms
YOLOv8+ResCBAM 1024 53.87M 196.2G 0.64 65.78% 8.7ms
YOLOv9 1024 69.42M 244.9G 0.66 65.62% 16.1ms
YOLOv8+GC 1024 43.85M 165.6G 0.66 66.32% 7.9ms

Citation

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

  @article{ju2024global,
    title={Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection},
    author={Ju, Rui-Yang and Chien, Chun-Tse and Lin, Chia-Min and Chiang, Jen-Shiun},
    journal={arXiv preprint arXiv:2407.03163},
    year={2024}
  }

Requirements

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

Environment

  pip install -r requirements.txt

Network Architecture

Dataset Download

  • You can download the GRAZPEDWRI-DX Dataset on this Link.

Dataset Split

  • To split the dataset into training set, validation set, and test set, you should first put the image and annotatation into ./GRAZPEDWRI-DX/data/images, and ./GRAZPEDWRI-DX/data/labels.

  • And then you can split the dataset as the following step:

      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
        └── data   
             ├── meta.yaml
             ├── 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
        └── data   
             ├── meta.yaml
             ├── 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
                       └── ...
    

Experimental setup

  • We have provided a training set, test set and validation set containing a single image that you can run directly by following the steps in the example below.
  • Before training the model, make sure the path to the data in the ./GRAZPEDWRI-DX/data/meta.yaml file is correct.
  # patch: /path/to/GRAZPEDWRI-DX/data
  path: 'E:/GRAZPEDWRI-DX/data'
  train: 'images/train_aug'
  val: 'images/valid'
  test: 'images/test'

Train

Arguments

You can set the value in the ./ultralytics/cfg/default.yaml.

Key Value Description
model None path to model file, i.e. yolov8m.yaml, yolov8m_GC_M1.yaml
data None path to data file, i.e. coco128.yaml, meta.yaml
epochs 100 number of epochs to train for, i.e. 100, 150
patience 50 epochs to wait for no observable improvement for early stopping of training
batch 16 number of images per batch (-1 for AutoBatch), i.e. 16, 32, 64
imgsz 640 size of input images as integer, i.e. 640, 1024
save True save train checkpoints and predict results
device 0 device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers 8 number of worker threads for data loading (per RANK if DDP)
pretrained True (bool or str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer 'auto' optimizer to use, choices=SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto
resume False resume training from last checkpoint
lr0 0.01 initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
momentum 0.937 SGD momentum/Adam beta1
weight_decay 0.0005 optimizer weight decay 5e-4
val True validate/test during training
  • Training Steps:
  python start_train.py -model path to model file --data_dir  path to data file
  • Example (YOLOv8+GC-M):
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GC.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml

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