VLMEvalKit Evaluation Results Collection
Explore how datasets shape classifier biases
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
Evaluate diversity in data sets to improve fairness
Browse and filter LLM benchmark results
statistics analysis for linear regression
What happened in open-source AI this year, and what’s next?
Build, preprocess, and train machine learning models
View monthly arXiv download trends since 1994
This project is a GUI for the gpustack/gguf-parser-go
Form for reporting the energy consumption of AI models.
Analyze data using Pandas Profiling
Display color charts and diagrams
The Open VLM Leaderboard is a data visualization tool designed to showcase the evaluation results of various Vision-Language Models (VLMs). It is part of the VLMEvalKit framework, enabling users to explore and compare the performance of different models across diverse datasets and metrics. The leaderboard provides a comprehensive overview of model effectiveness, helping researchers and practitioners identify top-performing models for specific tasks.
1. What is the purpose of the Open VLM Leaderboard?
The Open VLM Leaderboard is designed to provide a centralized platform for evaluating and comparing Vision-Language Models. It helps users identify the best-performing models for specific tasks and datasets.
2. Can I customize the metrics displayed on the leaderboard?
Yes, the leaderboard allows users to filter and customize the metrics displayed, enabling a focused analysis of model performance according to their needs.
3. How often are the leaderboard results updated?
The leaderboard is updated in real-time as new model evaluations are added to the VLMEvalKit framework. This ensures users always have access to the latest benchmark results.