Chest-Xray-Version2

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Chest-Xray-Version2

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Using RetinaNet with framework Detectron2 - Pytorch

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# Chest-Xray-Version2
***Status: done training, creating docker and client webserver***

The model detect the abnormalities in chest-Xray image by [Detectron2](https://github.com/facebookresearch/detectron2) - Pytorch.

## INSTALLATION 
1. Create virtual environment.
```bash
conda create -n chestxrayv2 python=3.7
conda activate chestxrayv2
```
2. clone this repository.
3. Install required packages. 
```bash 
pip install -r requirements.txt
```
4. Setup Detectron2.

See [installation instructions](https://detectron2.readthedocs.io/en/latest/tutorials/install.html). Or see [my instructions](https://github.com/DatacollectorVN/Detectron2-Tutorial).

## DATA PREPROCESSING
See [the document](https://docs.google.com/presentation/d/1oXhtmHP9GB1MmArHH-gxWWOc9mIvz-0N6M3GBCcqx0U/edit?usp=sharing) for understanding how we process the chest-Xray dataset from [VinBigdata](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/overview).

5. Download the standard and additional data after processing.
```bash
bash download_data_standard_add.sh
```

You can run file streamlit_.py for exploring the dataset in eda/ or nms-wbf-visualize/ .
  
*Note: Need to configure config/streamlit_eda.yaml file.*

## DOWNLOAD PRETRAIN MODEL:
You can download our model with [5 classes](https://github.com/DatacollectorVN/Chest-Xray-Version2/blob/master/experiments-records/README.md)

6. Download pretrain model with best mAP50 after 5000 epochs.
```bash
python experiments-records/download_5_classes_model.py
```

## FOR TRAINING
```bash 
python train.py
```
You need configure traininig in config/train.yaml.

## FOR EVALUATING
```bash
python eval.py
```
You need configure evaluating in config/inference.yaml.

### DEPLOY WEBSITE APPLICATION
```bash
streamlit run streamlit_inference.py
```
You need configure evaluating in config/inference.yaml.

Chest-Xray-Version2

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