Explore and benchmark visual document retrieval models
Benchmark LLMs in accuracy and translation across languages
Calculate VRAM requirements for LLM models
Explore GenAI model efficiency on ML.ENERGY leaderboard
Explain GPU usage for model training
Upload ML model to Hugging Face Hub
Evaluate code generation with diverse feedback types
Display and submit LLM benchmarks
Upload a machine learning model to Hugging Face Hub
Evaluate LLM over-refusal rates with OR-Bench
Find recent high-liked Hugging Face models
Convert PyTorch models to waifu2x-ios format
Display LLM benchmark leaderboard and info
Vidore Leaderboard is a tool designed for exploring and benchmarking visual document retrieval models. It provides a platform to compare and evaluate the performance of different models in the domain of visual document retrieval, helping users understand their strengths and weaknesses.
• Comprehensive Model Database: Access a wide range of pre-trained models for visual document retrieval. • Customizable Benchmarking: Define custom benchmarks to evaluate models based on specific criteria. • Performance Metrics: Detailed metrics to assess model accuracy, efficiency, and robustness. • Visual Results: Interactive visualizations to compare model performance side-by-side. • Community Sharing: Share benchmark results and insights with the broader AI research community.
What is visual document retrieval?
Visual document retrieval involves systems that retrieve documents based on visual content, such as images or layouts, rather than text-based search.
How do I interpret the performance metrics?
Performance metrics are provided in an easy-to-understand format, with visual charts and numerical scores to help compare model effectiveness.
Can I use Vidore Leaderboard for non-public models?
Yes, Vidore Leaderboard supports benchmarking private models by uploading them through the platform or API.