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
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
MaskTrackRCNN for video instance segmentation based on mmdetection

MaskTrackRCNN for video instance segmentation Introduction This repo serves as the official code release of the MaskTrackRCNN model for video instance

411 Jan 05, 2023
HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep.

HODEmu HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep. and emulates satellite abundance as a function of co

Antonio Ragagnin 1 Oct 13, 2021
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
[CVPR'2020] DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

DeepDeform (CVPR'2020) DeepDeform is an RGB-D video dataset containing over 390,000 RGB-D frames in 400 videos, with 5,533 optical and scene flow imag

Aljaz Bozic 165 Jan 09, 2023
The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution.

WSRGlow The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution. Audio sa

Kexun Zhang 96 Jan 03, 2023
Image marine sea litter prediction Shiny

MARLITE Shiny app for floating marine litter detection in aerial images. This directory contains the instructions and software needed to install the S

19 Dec 22, 2022
Project repo for the paper SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition (BMVC 2021) Project repo for the paper SILT: Self-supervised Lighting Trans

6 Dec 04, 2022
MAterial del programa Misión TIC 2022

Mision TIC 2022 Esta iniciativa, aparece como respuesta frente a los retos de la Cuarta Revolución Industrial, y tiene como objetivo la formación de 1

6 May 25, 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
Learning to Predict Gradients for Semi-Supervised Continual Learning

Learning to Predict Gradients for Semi-Supervised Continual Learning Code for project: "Learning to Predict Gradients for Semi-Supervised Continual Le

Yan Luo 2 Mar 05, 2022
Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".

HF2-VAD Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictio

76 Dec 21, 2022
SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches

SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches [Paper]  [Project Page]  [Interactive Demo]  [Supplementary Material]        Usag

215 Dec 25, 2022
Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Simple software to calculate susequence with highest sum This repository contain

Gbadamosi Farouk 1 Jan 31, 2022
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Yen-Chen Lin 3.2k Jan 08, 2023
Turning SymPy expressions into JAX functions

sympy2jax Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions. All SymPy floats become trainable input parameters. S

Miles Cranmer 38 Dec 11, 2022
Seg-Torch for Image Segmentation with Torch

Seg-Torch for Image Segmentation with Torch This work was sparked by my personal research on simple segmentation methods based on deep learning. It is

Eren Gölge 37 Dec 12, 2022
Java and SHACL code commented in the paper "Towards compliance checking in reified I/O logic via SHACL" submitted to ICAIL 2021

shRIOL The subfolder shRIOL contains Java files to execute the SHACL files on the OWL ontology. To compile the Java files: "javac -cp ./src/;./lib/* -

1 Dec 06, 2022
A task Provided by A respective Artenal Ai and Ml based Company to complete it

A task Provided by A respective Alternal Ai and Ml based Company to complete it .

Parth Madan 1 Jan 25, 2022
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022