Stars
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Rich is a Python library for rich text and beautiful formatting in the terminal.
Making large AI models cheaper, faster and more accessible
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
PyTorch Tutorial for Deep Learning Researchers
OpenMMLab Detection Toolbox and Benchmark
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
DSPy: The framework for programming—not prompting—language models
Open source platform for the machine learning lifecycle
Open standard for machine learning interoperability
Datasets, Transforms and Models specific to Computer Vision
Image augmentation for machine learning experiments.
Train transformer language models with reinforcement learning.
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
A little word cloud generator in Python
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Running large language models on a single GPU for throughput-oriented scenarios.
Keras implementations of Generative Adversarial Networks.
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
A collection of libraries to optimise AI model performances
🔥 2D and 3D Face alignment library build using pytorch
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents
Count the MACs / FLOPs of your PyTorch model.