Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection
| 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 |
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}
}
- Linux (Ubuntu)
- Python = 3.9
- Pytorch = 1.13.1
- NVIDIA GPU + CUDA CuDNN
pip install -r requirements.txt
- You can download the GRAZPEDWRI-DX Dataset on this Link.
-
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_idstored indataset.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 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 └── ...
- 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.yamlfile is correct.
# patch: /path/to/GRAZPEDWRI-DX/data
path: 'E:/GRAZPEDWRI-DX/data'
train: 'images/train_aug'
val: 'images/valid'
test: 'images/test'
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