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Model Benchmarking
Vidore Leaderboard

Vidore Leaderboard

Explore and benchmark visual document retrieval models

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What is Vidore Leaderboard ?

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.

Features

• 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.

How to use Vidore Leaderboard ?

  1. Visit the Vidore Leaderboard Platform: Access the tool through its official website or API.
  2. Select Models: Choose one or more models to benchmark from the available database.
  3. Define Benchmark Parameters: Specify the dataset, evaluation metrics, and any custom criteria.
  4. Run Benchmarking: Execute the benchmarking process to generate performance metrics.
  5. Analyze Results: Review and compare model performance using interactive visualizations.
  6. Share Insights: Download or share benchmark results with others for collaboration or further analysis.

Frequently Asked Questions

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.

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