Explore and benchmark visual document retrieval models
View RL Benchmark Reports
Search for model performance across languages and benchmarks
Retrain models for new data at edge devices
Calculate memory usage for LLM models
Convert Hugging Face model repo to Safetensors
Quantize a model for faster inference
Launch web-based model application
SolidityBench Leaderboard
Calculate survival probability based on passenger details
Browse and submit LLM evaluations
Leaderboard of information retrieval models in French
Create demo spaces for models on Hugging Face
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.