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+# How to config 
+
+The config file includes data path, optimizer, scheduler, etc, ...
+
+In each configure file: 
+- stages/data_params/root: To the folder where stores image data.
+- image_size: determine the size of image 
+
+Note:  
+
+You do not need to change: `train_csv` and `valid_csv` because they are overrided by running bash file bellow. 
+
+# Preprocessing 
+The following data is used for different models.
+
+* 3 windows (3w) data:
+    ```bash
+    python src/preprocessing.py extract-images --inputdir <kaggle_input_dir> --outputdir <output_folder>
+    ```
+
+* 3 windows (3w) with crop data:
+    ```bash
+    python src/preprocessing_3w.py extract-images --inputdir <kaggle_input_dir> --outputdir <output_folder>
+    ```
+
+* 3d data:
+    ```bash
+    python src/preprocessing2.py
+    ```
+
+
+# How to run  
+* Start docker: 
+    ```bash
+    make run
+    make exec 
+    cd /kaggle-rsna/
+    ```
+
+* Train `resnet18, resnet34, resnet50, alexnet` with `3 windows (3w)` setting:
+
+    ```bash
+    bash bin/train_bac_3w.sh 
+    ``` 
+    
+    Note: normalize=True
+
+* Train `resnet50` with `3d` setting:
+
+    ```bash
+    bash bin/train_bac_3d.sh 
+    ``` 
+    Note: normalize=False
+    
+* Train `densenet169` with `3 windows and crop` setting:
+
+    ```bash
+    bash bin/train_toan.sh 
+    bash bin/train_toan_resume.sh
+    ``` 
+    Note: normalize=True
+
+where: 
+- CUDA_VISIBLE_DEVICES: GPUs number required to train. 
+- LOGDIR: Output folder which stores the checkpoints, logs, etc. 
+- model_name: the name of model to be trained. The script supports the name of model in [here](https://github.com/creafz/pytorch-cnn-finetune)
+- It is better to create a `wandb` account, it will help you track your log, backup the code, store the checkpoints on the
+could in real-time. If you dont want to use `wandb`, please set: `WANDB=0`
+
+
+Output:  
+
+The best checkpoint is saved at: `${LOGDIR}/${log_name}/checkpoints/best.pth`. 
+
+# How to test  
+
+```bash
+python src/inference.py
+```
+Check function `predict_test_tta_ckp` for more information, you may want to change the path, the name of model and the output path.
+For `3d` setting, `normalization=False`, otherwise `normalization=True` 
+
+
+# Ensemble KFOLD 
+In `src/ensemble.py`, you should change the prediction path of each fold of model and the name of output ensemble. 
+```bash
+python src/ensemble.py
+```
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