Popular GitHub Repositories for Segmentation Transformer

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

??? 60+ Implementations/tutorials of deep learning papers with side-by-side notes ?; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ? reinforcement learning (ppo, dqn), capsnet, distillation, ... ?

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

A high-throughput and memory-efficient inference and serving engine for LLMs

Port of OpenAI's Whisper model in C/C++

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

OpenMMLab Detection Toolbox and Benchmark

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

deep learning for image processing including classification and object-detection etc.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

SOTA Open Source TTS

? A ranked list of awesome machine learning Python libraries. Updated weekly.

CVPR 2025 论文和开源项目合集

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

SGLang is a fast serving framework for large language models and vision language models.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Natural Language Processing Tutorial for Deep Learning Researchers

pix2tex: Using a ViT to convert images of equations into LaTeX code.

Image augmentation for machine learning experiments.