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---
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.
Dataset used: AllenTAN/image_sentiment
## 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 |