Scenic: A Jax Library for Computer Vision and Beyond

Overview

Scenic

Scenic is a codebase with a focus on research around attention-based models for computer vision. Scenic has been successfully used to develop classification, segmentation, and detection models for multiple modalities including images, video, audio, and multimodal combinations of them.

More precisely, Scenic is a (i) set of shared light-weight libraries solving tasks commonly encountered tasks when training large-scale (i.e. multi-device, multi-host) vision models; and (ii) a number of projects containing fully fleshed out problem-specific training and evaluation loops using these libraries.

Scenic is developed in JAX and uses Flax.

What we offer

Among others Scenic provides

  • Boilerplate code for launching experiments, summary writing, logging, profiling, etc;
  • Optimized training and evaluation loops, losses, metrics, bi-partite matchers, etc;
  • Input-pipelines for popular vision datasets;
  • Baseline models, including strong non-attentional baselines.

Papers using Scenic

Scenic can be used to reproduce the results from the following papers, which were either developed using Scenic, or have been reimplemented in Scenic:

Philosophy

Scenic aims to facilitate rapid prototyping of large-scale vision models. To keep the code simple to understand and extend we prefer forking and copy-pasting over adding complexity or increasing abstraction. Only when functionality proves to be widely useful across many models and tasks it may be upstreamed to Scenic's shared libraries.

Code structure

Shared libraries provided by Scenic are split into:

  • dataset_lib: Implements IO pipelines for loading and pre-processing data for common Computer Vision tasks and benchmarks. All pipelines are designed to be scalable and support multi-host and multi-device setups, taking care of dividing data among multiple hosts, incomplete batches, caching, pre-fetching, etc.
  • model_lib: Provides (i) several abstract model interfaces (e.g. ClassificationModel or SegmentationModel in model_lib.base_models) with task-specific losses and metrics; (ii) neural network layers in model_lib.layers, focusing on efficient implementation of attention and transfomer layers; and (iii) accelerator-friedly implementations of bipartite matching algorithms in model_lib.matchers.
  • train_lib: Provides tools for constructing training loops and implements several example trainers (classification trainer and segmentation trainer).
  • common_lib: Utilities that do not belong anywhere else.

Projects

Models built on top of Scenic exist as separate projects. Model-specific code such as configs, layers, losses, network architectures, or training and evaluation loops exist as separate projects.

Common baselines such as a ResNet or a Visual Transformer (ViT) are implemented in the projects/baselines project. Forking this directory is a good starting point for new projects.

There is no one-fits-all recipe for how much code should be re-used by projects. Projects can fall anywhere on the wide spectrum of code re-use: from defining new configs for an existing model to redefining models, training loop, logging, etc.

Getting started

  • See projects/baselines/README.md for a walk-through baseline models and instructions on how to run the code.
  • If you would like to to contribute to Scenic, please check out the Philisophy, Code structure and Contributing sections. Should your contribution be a part of the shared libraries, please send us a pull request!

Quick start

Download the code from GitHub

git clone https://github.com/google-research/scenic.git
cd scenic
pip install .

and run training for ViT on ImageNet:

python main.py -- \
  --config=projects/baselines/configs/imagenet/imagenet_vit_config.py \
  --workdir=./

Disclaimer: This is not an official Google product.

Owner
Google Research
Google Research
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

SUO-SLAM This repository hosts the code for our CVPR 2022 paper "Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation". ArXiv li

Robot Perception & Navigation Group (RPNG) 97 Jan 03, 2023
Bringing Characters to Life with Computer Brains in Unity

AI4Animation: Deep Learning for Character Control This project explores the opportunities of deep learning for character animation and control as part

Sebastian Starke 5.5k Jan 04, 2023
Official implementation of "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer"

[AAAI2022] UCTransNet This repo is the official implementation of "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspectiv

Haonan Wang 199 Jan 03, 2023
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Martin Marek 6 Mar 03, 2022
Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph This repository provides a pipeline to create a knowledge graph from ra

AWS Samples 3 Jan 01, 2022
GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021

ICLR Computational Geometry & Topology Challenge 2022 Welcome to the ICLR 2022 Computational Geometry & Topology challenge 2022 --- by the ICLR 2022 W

42 Dec 13, 2022
This GitHub repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

About Repository This repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.' About Code

Arun Verma 1 Nov 09, 2021
(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR (CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Paper Authors: Xiangtao Kong, Hengyuan

Xiangtao Kong 308 Jan 05, 2023
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

2 Apr 10, 2022
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 126 Jan 06, 2023
Exploring Simple Siamese Representation Learning

G-SimSiam A PyTorch implementation which refers to repo for the paper Exploring Simple Siamese Representation Learning by Xinlei Chen & Kaiming He Add

zhuyun 1 Dec 19, 2021
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

A 3D Generative Model for Structure-Based Drug Design Coming soon... Citation @inproceedings{luo2021sbdd, title={A 3D Generative Model for Structu

Shitong Luo 118 Jan 05, 2023
The end-to-end platform for building voice products at scale

Picovoice Made in Vancouver, Canada by Picovoice Picovoice is the end-to-end platform for building voice products on your terms. Unlike Alexa and Goog

Picovoice 318 Jan 07, 2023
RADIal is available now! Check the download section

Latest news: RADIal is available now! Check the download section. However, because we are currently working on the data anonymization, we provide for

valeo.ai 55 Jan 03, 2023
AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)

AOT-GAN for High-Resolution Image Inpainting Arxiv Paper | AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting Yanhong

Multimedia Research 214 Jan 03, 2023
Multiple-Object Tracking with Transformer

TransTrack: Multiple-Object Tracking with Transformer Introduction TransTrack: Multiple-Object Tracking with Transformer Models Training data Training

Peize Sun 537 Jan 04, 2023
Python implementation of ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images, AAAI2022.

ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images Binh M. Le & Simon S. Woo, "ADD:

2 Oct 24, 2022
Tools for investing in Python

InvestOps Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction This is a Python package with simple and effective

24 Nov 26, 2022
A framework for attentive explainable deep learning on tabular data

🧠 kendrite A framework for attentive explainable deep learning on tabular data 💨 Quick start kedro run 🧱 Built upon Technology Description Links ke

Marnix Koops 3 Nov 06, 2021