Stars
A library for efficient similarity search and clustering of dense vectors.
Get your documents ready for gen AI
Supercharge Your LLM Application Evaluations 🚀
PyTorch implementation of FractalGen https://arxiv.org/abs/2502.17437
Genome modeling and design across all domains of life
Segment Anything for Histopathology
🤗 smolagents: a barebones library for agents that think in code.
TiffSlide - cloud native openslide-python replacement based on tifffile
Async-first meta framework for building enterprise-grade multi-agent systems.
Implementation of Alphafold 3 from Google Deepmind in Pytorch
Model interpretability and understanding for PyTorch
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Implementation of Attention-based Deep Multiple Instance Learning in PyTorch
A specification that python filesystems should adhere to.
A game theoretic approach to explain the output of any machine learning model.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Python library for converting WSI files to DICOM
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Website for the BioImage Model zoo -- a model zoo for bioimage analysis.
Stable diffusion for real-time music generation
📐 Jekyll theme for building a personal site, blog, project documentation, or portfolio.
🌐 Jekyll is a blog-aware static site generator in Ruby
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)