This is a GitHub repository for a software project named "simplex-signals." It includes code and resources for machine learning models focused on test-time scaling and evaluating performance across different datasets. The repository contains Python files and instructions for usage, training, inference, and evaluation of models.
Features
Test-Time Scaling
This feature allows for simple scaling of inputs during test-time to improve the performance of machine learning models. It is designed to be easy to implement without requiring retraining.
Integration with NumPy
The solution works seamlessly with NumPy arrays, allowing for efficient scaling operations and facilitating integration with existing scientific computing workflows.
Compatibility with Multiple Models
This tool can be used with various pre-trained models, making it versatile for different applications. It supports models like EfficientNet and ResNet.
Evaluation Tools
Provides tools to evaluate the performance of scaled models against benchmark datasets, aiding in understanding the impact of scaling on model accuracy.
Pre-trained Model Support
The solution supports pre-trained models, allowing users to improve their models' performance without needing to collect large amounts of new training data.