This documentation provides information about the method of solving RSNA Pneumonia Detection Challenge kaggle competition, including the data used, the methods and ideas employed, and the metrics achieved. It also includes usage instructions and author information.
In this challenge competitors are predicting whether pneumonia exists in a given image. They do so by predicting bounding boxes around areas of the lung. Samples without bounding boxes are negative and contain no definitive evidence of pneumonia. Samples with bounding boxes indicate evidence of pneumonia. The dataset used for training and scoring is loaded with pytorch.
The RSNA Pneumonia Detection Challenge neural network model is built using the YOLOv8n architecture.
Train YOLOv8n on 100 epochs and scale bboxes to 0.17 and create submission.csv
After training, the best model achieved 0.11939 on private score and 0.04166 public score.
- Ubuntu 20.04
- Python 3.10
Clone repository
git clone https://github.com/SoulHb/RSNA.gitMove to project folder
cd RSNACreate conda env
conda create --name=rsna python=3.10Activate virtual environment
conda activate rsna Install pip
conda install pip Install dependencies
pip install -r requirements.txtThe model is trained on the provided dataset using the following configuration:
- Optimizer: AdamW
- Learning rate: 0.002
- Batch size: 16
- Number of epochs: 100
Move to src folder
cd srcRun train.py
python train.py --data_path /path/to/data/folder --yaml_path /path/to/dataset.yaml --epochs 100 --batch_size 16
To use the trained model for RSNA Pneumonia Detection Challenge, follow the instructions below:
Move to src folder
cd srcRun inference
python inference.py --data_path /path/to/data/folder --best_model_path /path/to/best/model --submission_path /path/to/submission.csvThis RSNA Pneumonia Detection Challenge project was developed by Namchuk Maksym. If you have any questions, please contact me: namchuk.maksym@gmail.com