UniBench: A Python Library to Evaluate Vision-Language Models VLMs Robustness Across Diverse Benchmarks

UniBench is a platform that evaluates vision-language models (VLMs) by implementing 53 diverse benchmarks in a user-friendly codebase. It categorizes these benchmarks into seven types and seventeen capabilities, providing a comprehensive evaluation framework for VLMs. Key Insights: - VLMs excel in some areas but struggle with others, showing wide performance variations across tasks. - Scaling model size and training data improves performance in many areas, but has limited benefits for visual relations and reasoning tasks. - VLMs surprisingly struggle with simple numerical tasks like MNIST digit recognition. - Data quality is emphasized over quantity, and tailored learning objectives can significantly impact performance. Practical Solutions: UniBench offers a distilled set of representative benchmarks that can be run quickly on standard hardware, streamlining VLM evaluation for more meaningful comparisons and insights into effective research strategies. UniBench is a Python library that evaluates VLMs' robustness across diverse benchmarks, providing a practical tool for companies looking to leverage AI for competitive advantage. If you want to evolve your company with AI, UniBench can help redefine your way of work. It can be used to identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice, connect with us at hello@itinai.com. Follow us on Twitter @itinaicom and join our Telegram Channel and LinkedIn Group for continuous insights into leveraging AI. Useful Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom