🎨 AI-Powered Art Classification

An advanced deep learning-based system for classifying artwork images into 30 distinct artistic styles, including Cubism, Surrealism, Impressionism, and more. This project leverages state-of-the-art CNN architectures to achieve high-precision art recognition.

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🚀 Features

• Trained on 7,315+ artwork images across 30 artistic styles.
• Implemented deep learning models: ResNet152V2, Xception, and InceptionV3.
• Achieved 97% accuracy using ResNet152V2.
• Explainability with Grad-CAM: Generates heatmaps to visualize model decisions.
• Potential applications in museum archiving, e-commerce, and AI-powered art education.

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📂 Dataset

The dataset used for this project can be found here: Surreal Symphonies - A Dataset of Diverse Art.

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📌 Model Architectures

The following pre-trained deep learning models were fine-tuned for the classification task:

• ResNet152V2 (Best Performer: 97% Accuracy)
• Xception
• InceptionV3

These models were trained and evaluated to determine the most efficient approach for accurate art classification.

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🔍 Explainability with Grad-CAM

To ensure transparency in AI-driven decisions, Grad-CAM (Gradient-weighted Class Activation Mapping) was used to generate heatmaps, highlighting the key areas in images that influenced the model’s predictions.

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🛠 Installation & Setup

Clone the repository:

git clone https://github.com/your-username/ai-art-classification.git
cd ai-art-classification

Install dependencies:

pip install -r requirements.txt

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🎯 Training the Model

To train the model, run the following command:

python train.py --model resnet152v2 --epochs 20 --batch_size 32

For other models, specify --model xception or --model inceptionv3.

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📊 Evaluation

Evaluate the trained model on the test dataset:

python evaluate.py --model resnet152v2

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📈 Grad-CAM Visualization

To generate heatmaps for model predictions:

python grad_cam.py --image sample.jpg --model resnet152v2

This will output a visualization of the regions in the image that contributed to the classification.

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🔮 Future Applications

This AI-powered classification system can be expanded to various domains:

• 🏛️ Museum Archiving: Digitally categorize and preserve artworks.
• 🛍️ E-commerce: Enable style-based search for art marketplaces.
• 📚 AI-powered Art Education: Assist students in learning about different artistic styles.

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🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

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📜 License

This project is open-source and available under the MIT License.

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📩 Contact

For any inquiries or collaborations, reach out at your-email@example.com.

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