PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Overview

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG)

This repository contains a PyTorch implementation of the paper Convolutional Networks with Adaptive Inference Graphs presented at ECCV 2018.

The code is based on the PyTorch example for training ResNet on Imagenet.

Table of Contents

  1. Introduction
  2. Usage
  3. Citing
  4. Requirements
  5. Contact

Introduction

Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which layer to compute next.

Convolutional networks with adaptive inference graphs (ConvNet-AIG) can adaptively define their network topology conditioned on the input image. Following a high-level structure similar to residual networks (ResNets), ConvNet-AIG decides for each input image on the fly which layers are needed. In experiments on ImageNet we show that ConvNet-AIG learns distinct inference graphs for different categories.

Usage

There are two training files. One for CIFAR-10 train.py and one for ImageNet train_img.py.

The network can be simply trained with python train.py or with optional arguments for different hyperparameters:

$ python train.py --expname {your experiment name}

For ImageNet the folder containing the dataset needs to be supplied

$ python train_img.py --expname {your experiment name} [imagenet-folder with train and val folders]

Training progress can be easily tracked with visdom using the --visdom flag. It keeps track of the learning rate, loss, training and validation accuracy as well as the activation rates of the gates for each class.

By default the training code keeps track of the model with the highest performance on the validation set. Thus, after the model has converged, it can be directly evaluated on the test set as follows

$ python train.py --test --resume runs/{your experiment name}/model_best.pth.tar

Requirements

This implementation is developed for

  1. Python 3.6.5
  2. PyTorch 0.3.1
  3. CUDA 9.1

Target Rate schedules

To improve performance and memory efficiency, the target rates of early, last and downsampling layers can be fixed so as to always execute the layers. Specifically, for the results in the paper the following target rate schedules are used for ResNet 50: [1, 1, 0.8, 1, t, t, t, 1, t, t, t, t, t, 1, 0.7, 1] for t in [0.4, 0.5, 0.6, 0.7] For ResNet 101 the following rates can be used: ([1]* 8).extend([t] * 25) for t in [0.3, 0.5]

For compatibility to newer versions, please make a pull request.

Citing

If you find this helps your research, please consider citing:

@conference{Veit2018,
title = {Convolutional Networks with Adaptive Inference Graphs},
author = {Andreas Veit and Serge Belongie},
year = {2018},
journal = {European Conference on Computer Vision (ECCV)},
}

Contact

andreas at cs dot cornell dot edu

Owner
Andreas Veit
Research Scientist at Google Research in New York City
Andreas Veit
Tutorial for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop

Workshop Advantech Jetson Nano This tutorial has been designed for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop in collaboration with Adva

Edge Impulse 18 Nov 22, 2022
This repository is for our EMNLP 2021 paper "Automated Generation of Accurate & Fluent Medical X-ray Reports"

Introduction: X-Ray Report Generation This repository is for our EMNLP 2021 paper "Automated Generation of Accurate & Fluent Medical X-ray Reports". O

no name 36 Dec 16, 2022
Official implementation for Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

Multi-modal Interaction Graph Convolutioal Network for Temporal Language Localization in Videos Official implementation for Multi-Modal Interaction Gr

Zongmeng Zhang 15 Oct 18, 2022
Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data.

Deep Learning Dataset Maker Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data. How to use Down

deepbands 25 Dec 15, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
TrackFormer: Multi-Object Tracking with Transformers

TrackFormer: Multi-Object Tracking with Transformers This repository provides the official implementation of the TrackFormer: Multi-Object Tracking wi

Tim Meinhardt 321 Dec 29, 2022
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022
A High-Performance Distributed Library for Large-Scale Bundle Adjustment

MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment This repo contains an official implementation of MegBA. MegBA is a

旷视研究院 3D 组 336 Dec 27, 2022
I3-master-layout - Simple master and stack layout script

Simple master and stack layout script | ------ | ----- | | | | | Ma

Tobias S 18 Dec 05, 2022
Code for pre-training CharacterBERT models (as well as BERT models).

Pre-training CharacterBERT (and BERT) This is a repository for pre-training BERT and CharacterBERT. DISCLAIMER: The code was largely adapted from an o

Hicham EL BOUKKOURI 31 Dec 05, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
なりすまし検出(anti-spoof-mn3)のWebカメラ向けデモ

FaceDetection-Anti-Spoof-Demo なりすまし検出(anti-spoof-mn3)のWebカメラ向けデモです。 モデルはPINTO_model_zoo/191_anti-spoof-mn3からONNX形式のモデルを使用しています。 Requirement mediapipe

KazuhitoTakahashi 8 Nov 18, 2022
Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Code To run: python runner.py new --save SAVE_NAME --data PATH_TO_DATA_DIR --dataset DATASET --model model_name [options] --n 1000 - train - t

Geoff Pleiss 5 Dec 12, 2022
The Official Repository for "Generalized OOD Detection: A Survey"

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

Jingkang Yang 338 Jan 03, 2023
Pre-trained BERT Models for Ancient and Medieval Greek, and associated code for LaTeCH 2021 paper titled - "A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek"

Ancient Greek BERT The first and only available Ancient Greek sub-word BERT model! State-of-the-art post fine-tuning on Part-of-Speech Tagging and Mor

Pranaydeep Singh 22 Dec 08, 2022
Sandbox for training deep learning networks

Deep learning networks This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (

Oleg Sémery 2.7k Jan 01, 2023
HGCN: Harmonic Gated Compensation Network For Speech Enhancement

HGCN The official repo of "HGCN: Harmonic Gated Compensation Network For Speech Enhancement", which was accepted at ICASSP2022. How to use step1: Calc

ScorpioMiku 33 Nov 14, 2022
Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Environment Inference for Invariant Learning This code accompanies the paper Environment Inference for Invariant Learning, which appears at ICML 2021.

Elliot Creager 40 Dec 09, 2022
Indices Matter: Learning to Index for Deep Image Matting

IndexNet Matting This repository includes the official implementation of IndexNet Matting for deep image matting, presented in our paper: Indices Matt

Hao Lu 357 Nov 26, 2022
retweet 4 satoshi ⚡️

rt4sat retweet 4 satoshi This bot is the codebase for https://twitter.com/rt4sat please feel free to create an issue if you saw any bugs basically thi

6 Sep 30, 2022