Stars
🤗 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
TensorFlow code and pre-trained models for BERT
A toolkit for developing and comparing reinforcement learning algorithms.
LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source …
Graph Neural Network Library for PyTorch
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
End-to-End Object Detection with Transformers
An open-source NLP research library, built on PyTorch.
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.…
TensorFlow-based neural network library
Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
Implementation of Graph Convolutional Networks in TensorFlow
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
Google Cloud Client Library for Python
Starter code for working with the YouTube-8M dataset.
The Places365-CNNs for Scene Classification
Minimal, clean example of lstm neural network training in python, for learning purposes.
Shared repository for open-sourced projects from the Google AI Language team.
Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)
Simple script for downloading Youtube comments without using the Youtube API
Theano-based implementation of Deep Q-learning
Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"
[ECCV 2018] Official code for "Graph R-CNN for Scene Graph Generation"