Start a web application for model inference
Display LLM benchmark leaderboard and info
Explain GPU usage for model training
Calculate survival probability based on passenger details
Push a ML model to Hugging Face Hub
View NSQL Scores for Models
Display leaderboard for earthquake intent classification models
Create demo spaces for models on Hugging Face
Convert Hugging Face models to OpenVINO format
Download a TriplaneGaussian model checkpoint
Compare model weights and visualize differences
View LLM Performance Leaderboard
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Mapcoordinates is a web application designed for model benchmarking. It provides a platform to evaluate and compare the performance of various machine learning models by analyzing their inference capabilities across different datasets and scenarios. This tool is particularly useful for researchers and developers looking to optimize model performance and make data-driven decisions.
• Model Evaluation: Comprehensive analysis of model inference capabilities
• Cross-Model Comparison: Direct comparison of different models' performance
• Customizable Metrics: Define and track specific evaluation criteria
• Data Visualization: Intuitive graphs and charts to represent benchmarking results
• Cross-Framework Support: Compatibility with multiple machine learning frameworks
• Scenario Simulation: Test models under various real-world scenarios
What frameworks does Mapcoordinates support?
Mapcoordinates supports a wide range of machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn.
Can I customize the evaluation metrics?
Yes, Mapcoordinates allows you to define custom evaluation metrics to align with your specific benchmarking goals.
How long does the benchmarking process typically take?
The duration depends on the model complexity and dataset size. .Simple models may complete in minutes, while larger models can take hours.