frgfm/holocron

What it solves

Holocron is a library of high-quality implementations of recent deep learning techniques for computer vision. It provides a centralized collection of specialized PyTorch layers, loss functions, and model architectures that would otherwise require searching for and implementing them from various research papers.

How it works

The library integrates directly into the PyTorch ecosystem, allowing users to pair these implementations with their own frameworks and model zoos. It provides a variety of a pre-trained models and components, including:

• Layers & Components: Specialized activation functions (e.g., HardMish), convolutions (e.g., NormConv2d), pooling (e.g., BlurPool2d), and attention mechanisms (e.g., SAM).
• Architectures: Implementations of models for image classification (e.g., RepVGG, ConvNeXt), object detection (YOLOv1-v4), and semantic segmentation (U-Net, UNet++).
• Optimizers: Alternative optimizers to Adam, such as LARS, Lamb, and Adan.

Who it’s for

It is designed for computer vision researchers and developers who need reliable, ready-to-use implementations of state-of-the-art vision models and layers to accelerate their development and experimentation.

Highlights

• Broad Component Library: Includes a wide array of activation functions, loss functions, convolutions, and optimizers.
• Hugging Face Integration: Offers a live demo app hosted on Hugging Face Spaces.
• Latency Benchmarks: Provides detailed GPU/CPU latency data for supported models to help users choose the right architecture for their hardware.
• Deployment Ready: Includes a Dockerfile and a FastAPI template for quickly deploying models as REST APIs.