Leaderboard for text-to-video generation models
Generate images based on data
Transfer GitHub repositories to Hugging Face Spaces
Generate benchmark plots for text generation models
Create a detailed report from a dataset
Generate detailed data profile reports
Finance chatbot using vectara-agentic
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
Profile a dataset and publish the report on Hugging Face
Make RAG evaluation dataset. 100% compatible to AutoRAG
Analyze and visualize your dataset using AI
Explore speech recognition model performance
Generate a data profile report
VideoScore Leaderboard is a data visualization tool designed to evaluate and compare the performance of text-to-video generation models. It provides a centralized platform to display leaderboard tables with video scores and evaluation data, helping users track model performance, identify top-performing models, and gain insights into model strengths and weaknesses.
• Interactive Tables: Sort and filter data to focus on specific models or metrics.
• Customizable Filters: Narrow down results by evaluation criteria, model names, or date ranges.
• Real-Time Updates: Stay current with the latest model evaluations and scores.
• Visual Analytics: Gain deeper insights with charts and graphs that highlight performance trends.
• Model Comparison: Directly compare multiple models side-by-side for comprehensive analysis.
• Export Options: Download data in various formats for offline reviewing or reporting.
What models are supported by VideoScore Leaderboard?
VideoScore Leaderboard supports a wide range of text-to-video generation models, including popular ones like Pika Labs, Kaiber, and Pix2Vid.
Can I customize the metrics displayed in the leaderboard?
Yes, you can customize the metrics displayed by using the filtering options available in the tool. This allows you to focus on the specific evaluation criteria you care about.
How frequently is the leaderboard updated?
The leaderboard is updated in real-time as new evaluation data becomes available, ensuring you always have the most current information on model performance.