An Open Source Machine Learning Framework for Everyone

🤗 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.

Models and examples built with TensorFlow

Google Research

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

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

Latex code for making neural networks diagrams

This repository contains implementations and illustrative code to accompany DeepMind publications

StyleGAN - Official TensorFlow Implementation

Python package built to ease deep learning on graph, on top of existing DL frameworks.

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

StyleGAN2 - Official TensorFlow Implementation

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

Code release for NeRF (Neural Radiance Fields)

Image-to-image translation with conditional adversarial nets

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

TensorFlow-based neural network library

A faster pytorch implementation of faster r-cnn

Semantic Image Synthesis with SPADE

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

Taming Transformers for High-Resolution Image Synthesis

COCO API - Dataset @ http://cocodataset.org/

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Build Graph Nets in Tensorflow

Graph Convolutional Networks in PyTorch

StarGAN - Official PyTorch Implementation (CVPR 2018)

Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets https://phillipi.github.io/pix2pix/