Build Enterprise Datasets with CLIP for Multimodal Model Training Using Intel Gaudi2 HPUs

James Gallagher's guide explores the process of deduplicating image datasets using OpenAI’s Contrastive Language–Image Pre-training (CLIP) model on Intel's Habana Gaudi2 chip, which is optimized for high-performance computer vision tasks. Deduplication is essential for improving the accuracy of multimodal models by removing near-duplicate images that provide limited value, thus reducing model training time and enhancing dataset quality. The guide outlines the steps for installing CLIP, calculating image embeddings, and using Euclidean distance to identify and remove similar images from a dataset, with a focus on a logistics-themed dataset containing nearly 20,000 images. It emphasizes the importance of maintaining the quality of data over mere quantity and suggests using methods like image hashing for exact duplicates while employing CLIP for near duplicates. The guide also discusses the advantages of using vector databases for larger datasets and highlights the iterative nature of model training to adapt to changing requirements and environments, encouraging ongoing refinement through techniques like active learning.