Display and analyze PyTorch Image Models leaderboard
What happened in open-source AI this year, and what’s next?
Explore and filter model evaluation results
Display a Bokeh plot
Analyze your dataset with guided tools
Transfer GitHub repositories to Hugging Face Spaces
Generate benchmark plots for text generation models
Profile a dataset and publish the report on Hugging Face
Check system health
Analyze and compare datasets, upload reports to Hugging Face
Display document size plots
Generate a co-expression network for genes
Analyze weekly and daily trader performance in Olas Predict
The timm Leaderboard is a data visualization tool designed to display and analyze PyTorch Image Models (timm) performance metrics. It provides a centralized platform to track and compare the effectiveness of various image models, helping users make informed decisions for their projects. The leaderboard is tailored for developers, researchers, and enthusiasts working with image models.
• Real-time Tracking: Stay updated with the latest performance metrics of models in the timm library.
• Interactive Interface: Easily filter, sort, and visualize model performance based on your criteria.
• Multiple Metrics Support: Compare models across different benchmarks and evaluation criteria.
• Historical Analysis: Access performance trends over time to understand model improvements.
• CustomizableViews: Tailor the leaderboard to focus on specific metrics or model categories.
• Community-Driven: Contributions from the community ensure a diverse and comprehensive dataset.
What models are included in The timm Leaderboard?
The leaderboard includes a wide range of image models from the PyTorch Image Models (timm) library, covering various architectures and pre-trained weights.
How do I filter models based on specific criteria?
Use the filtering options available on the leaderboard to narrow down models by metrics, architecture, or other attributes.
How often is the leaderboard updated?
The leaderboard is updated regularly to reflect the latest additions and improvements in the timm library and its models.