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Google Research

This repository contains code released by Google Research.

All datasets in this repository are released under the CC BY 4.0 International license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file.


Because the repo is large, we recommend you download only the subdirectory of interest:

SUBDIR=foo
svn export https://github.com/google-research/google-research/trunk/$SUBDIR

If you'd like to submit a pull request, you'll need to clone the repository; we recommend making a shallow clone (without history).

git clone [email protected]:google-research/google-research.git --depth=1

Disclaimer: This is not an official Google product.

Updated in 2023.

Google Research's Projects

multinerf icon multinerf

A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF

nasbench icon nasbench

NASBench: A Neural Architecture Search Dataset and Benchmark

nerf-from-image icon nerf-from-image

Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion

nisaba icon nisaba

Finite-state script normalization and processing utilities

noisystudent icon noisystudent

Code for Noisy Student Training. https://arxiv.org/abs/1911.04252

open-covid-19-data icon open-covid-19-data

Open source aggregation pipeline for public COVID-19 data, including hospitalization/ICU/ventilator numbers for many countries.

pangea icon pangea

Panoramic Graph Environment Annotation toolkit, for collecting audio and text annotations in panoramic graph environments such as Matterport3D and StreetLearn.

pisac icon pisac

Tensorflow 2 source code for the PI-SAC agent from "Predictive Information Accelerates Learning in RL" (NeurIPS 2020)

pix2seq icon pix2seq

Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion)

planet icon planet

Learning Latent Dynamics for Planning from Pixels

plur icon plur

PLUR (Programming-Language Understanding and Repair) is a collection of source code datasets suitable for graph-based machine learning. We provide scripts for downloading, processing, and loading the datasets. This is done by offering a unified API and data structures for all datasets.

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