Get up and running with Kimi-K2.5, GLM-4.7, DeepSeek, gpt-oss, Qwen, Gemma and other models.

🤗 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.

Animation engine for explanatory math videos

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

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.

Datasets, Transforms and Models specific to Computer Vision

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

A collection of resources and papers on Diffusion Models

starter from "How to Train a GAN?" at NIPS2016

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

Adding guardrails to large language models.

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

A unified evaluation framework for large language models

Unsupervised Learning for Image Registration

Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

Medical imaging processing for AI applications.

3D U-Net model for volumetric semantic segmentation written in pytorch

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Painlessly create beautiful matplotlib plots.

Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet

Data Augmentation For Object Detection