#CLIP models

Explore a comprehensive vector search engine that enhances traditional vector databases by integrating machine learning deployment, efficient input processing, and adaptable search settings. With a simple API, it smoothens the workflow from vector creation to retrieval, supporting both text and image searches. Utilizing cutting-edge embeddings from top-tier ML models, it offers robust CPU and GPU compatibility for superior performance and expansion potential. Discover compatibility with AI frameworks such as Haystack, Griptape, and Langchain, while benefiting from optimized cloud services and continuous support.

This competition aims to design effective datasets for pre-training CLIP models, prioritizing dataset curation. Participants focus on achieving high accuracy in downstream tasks by selecting optimal image-text pairs, with a fixed model setup. The competition offers two tracks, allowing varying computational resources: one with a provided data pool and another that accepts additional external data. With scales from small to xlarge, it covers different computational demands. The project offers tools for downloading, selecting subsets, training, and evaluation to support flexible and robust participation.