dermy-models/README.md

# Dermy Image Classification Model

This repository contains a Python script for training and compiling the dermy image classification model variants using the tflite model maker.
The compiled models are stored in the `models/` subfolder.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Data Sources](#data-sources)
- [Model Compilation](#model-compilation)
- [License](#license)

## Installation {#installation}

To get started, clone this repository and install the required dependencies.

```bash
git clone https://vcs.r0r-5chach.xyz/r0r-5chach/dermy-models.git
cd your-repo-name
pip install -r requirements.txt
```

## Usage {#usage}

To train and compile the binary classification model, run the `binary-compile.py` script. 
This script will process the data, train the model, and save the compiled model to the `models/` subfolder.

```bash
python binary-compile.py
```

## Project Structure {#project-structure}

```bash
dermy-model/
├── README.md
├── LICENSE
├── requirements.txt
├── binary-compile.py
└── models/
    └── binary-classifiers/
        ├── dermy-efficientnet_lite3.tflite
        ├── dermy-efficientnet_lite4.tflite
        └── dermy-mobilenet_v2.tflite
```

## Data Sources {#data-sources}

The data used to train the models in this repository was sourced from [Kaggle](https://www.kaggle.com/).

The specific dataset for the binary classifier can be found [here](https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign)

## Model Compilation {#model-compilation}

The `binary-compile.py` script handles the model training and compilation process. The compiled models are saced in the `models/` folder.

```python
from tflite_model_maker import model_spec
from tflite_model_maker import image_classifier
from tflite_model_maker.config import ExportFormat
from tflite_model_maker.config import QuantizationConfig
from tflite_model_maker.image_classifier import DataLoader

DATA = "data/binary-classification/"
MODELS = ["mobilenet_v2", "efficientnet_lite3", "efficientnet_lite4"]

train_data = DataLoader.from_folder(DATA + "train")
test_data = DataLoader.from_folder(DATA + "test")
train_data, valid_data = train_data.split(0.8)


for i in range(len(MODELS)):
    model = image_classifier.create(train_data,
                                    validation_data=valid_data,
                                    model_spec=model_spec.get(MODELS[i]),
                                    epochs=50,
                                    learning_rate=0.0001,
                                    dropout_rate=0.2,
                                    batch_size=64,
                                    use_augmentation=True)
    model.summary()

    loss, accuracy = model.evaluate(test_data)

    config = QuantizationConfig.for_float16()
    filename = f"dermy-binary-classification-{MODELS[i]}.tflite"

    model.export(export_dir="./models",
                 export_format=ExportFormat.TFLITE,
                 tflite_filename=filename,
                 quantization_config=config)
```


## License {#license}

This project is licensed under the GNU GPLv3 License. See the [LICENSE](LICENSE) file for more details.