frgfm/holocron

What it solves

Holocron provides a collection of modern deep learning techniques and model architectures specifically for computer vision tasks. It bridges the gap between academic research papers and practical implementation by offering standardized, PyTorch-compatible code for layers, loss functions, and optimization algorithms that are often difficult to find or implement from scratch.

How it works

It functions as a library of modular components that can be integrated into existing PyTorch workflows. Users can load pre-trained models (such as ResNet, ReXNet, or YOLO variants), utilize custom loss functions like PolyLoss or Focal Loss, and apply specialized layers or optimizers. The project includes reference training scripts for classification, detection, and segmentation, as well as tools for benchmarking model latency on different hardware.

Who it’s for

This project is for researchers and developers working with computer vision in PyTorch who want to experiment with state-of-the-art architectures and training tricks without having to implement them manually from research papers.

Highlights

• Extensive library of vision-specific components including activation functions, loss functions, and specialized convolution layers.
• Support for various computer vision tasks such as image classification, object detection, and semantic segmentation.
• Includes a suite of modern optimizers like LARS, Lamb, and AdamP.
• Provides latency benchmarking tools to help users evaluate model performance on CPU and GPU.
• Includes a minimal API template using FastAPI for deploying models.