Deep Learning × Art:
Model Accuracy Improvement Experiment

This research project focused on improving the accuracy of image captioning models specifically for the art domain. By fine-tuning pre-trained models on a curated dataset of artworks, I aimed to enhance the model's ability to generate accurate and contextually relevant captions for paintings and other artistic works. The experiment involved collecting and preprocessing a specialized dataset of artworks, fine-tuning transformer-based models using transfer learning techniques, and evaluating performance using various metrics. The project demonstrated the effectiveness of domain-specific fine-tuning in improving model accuracy for specialized use cases. Key achievements included successfully adapting general-purpose image captioning models to the art domain, achieving improved accuracy metrics, and documenting the fine-tuning process and results for future reference.

• •Image captioning model fine-tuning
• •Art domain dataset preparation
• •Model evaluation and metrics
• •Transfer learning techniques
• •Performance optimization