骨折检测-基于YOLOv9实现的X光图像中的手臂骨折检测算法-附项目源码-优质项目实战.zip

# YOLOv9 for Fracture Detection ## Comparison <p align="left"> <img src="img/figure_comparison.jpg" width="480" title="details"> </p> ## Performance | Model | Test Size | Param. | FLOPs | F1 Score | AP₅₀^val | AP_50-95^val | 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** | ## Requirements * Linux (Ubuntu) * Python = 3.9 * Pytorch = 1.13.1 * NVIDIA GPU + CUDA CuDNN ## Environment ``` pip install -r requirements.txt ``` ## Overall Flowchart <p align="left"> <img src="img/figure_flowchart.jpg" width="1024" title="details"> </p> ## Dataset Split * GRAZPEDWRI-DX Dataset [(Download Link)](https://figshare.com/articles/dataset/GRAZPEDWRI-DX/14825193) * 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](https://github.com/WongKinYiu/yolov9). * 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 ```