README.md

metadata

title: effnetb2-sentiment-analysis
emoji: ✨
app_file: src/app.py
colorFrom: red
colorTo: gray
sdk: gradio
pinned: false
license: mit
datasets:
  - AllenTAN/image_sentiment
base_model: google/efficientnet-b2

EfficientNet B2 Image Classification

This project implements an image classification model using the EfficientNet B2 architecture, fine-tuned on a custom dataset. It provides a modular and easy-to-use structure for training and evaluating the model.

Project Structure

project_root/
│
├── data/
│   ├── train/
│   └── test/
│
├── src/
│   ├── __init__.py
│   ├── data_setup.py
│   ├── train_and_test.py
│   ├── model.py
│
├── main.py
├── requirements.txt
└── README.md

• data/: Contains the training and testing datasets.
• src/: Source code for the project.
• main.py: The entry point of the project.

Setup

1. Clone the repository:

   git clone https://github.com/brepositorium/effnetb2-sentiment-analysis.git
   cd effnetb2-sentiment-analysis
   

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Usage

To train the model, run:

python main.py

This will start the training process using the EfficientNet B2 model on your dataset. The script will output training progress and final results.

Customization

• Edit src/model.py to experiment with different model architectures or layer configurations.
• Adjust data augmentation in src/data_setup.py if needed.

Results

After training, the model will output training and validation accuracy and loss. You can find these results printed in the console output.

Contributing

Feel free to open issues or submit pull requests if you have suggestions for improvements or encounter any problems.

License

MIT License