Ultimate Guide to Using CLIP with Intel Gaudi2

Contrastive Language Image Pretraining (CLIP) is a versatile multimodal vision model architecture developed by OpenAI, used for tasks like image and video classification, semantic search, and dataset deduplication. The guide by James Gallagher explores how to enhance CLIP models for specific use cases using Intel's Gaudi2 hardware and the Hugging Face Transformers library. It outlines the process of training a projection layer on a custom dataset using the COCO dataset as an example, highlighting the efficiency gains with Gaudi2, which can compute thousands of CLIP vectors per minute. The guide also provides insights into deploying CLIP models for enterprise applications, emphasizing the potential for real-time applications and the benefits of using Gaudi2's AI acceleration for both training and inference. Furthermore, it includes benchmarks demonstrating Gaudi2's performance in computing CLIP vectors, making it suitable for various real-time and batch processing tasks in computer vision.