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
Script for getting information in discord

User-info.py Script for getting information in https://discord.com/ Instalação: apt-get update -y apt-get upgrade -y apt-get install git pkg install

Moleey 1 Dec 18, 2021
PINN Burgers - 1D Burgers equation simulated by PINN

PINN(s): Physics-Informed Neural Network(s) for Burgers equation This is an impl

ShotaDEGUCHI 1 Feb 12, 2022
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
StarGAN - Official PyTorch Implementation (CVPR 2018)

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Yunjey Choi 5.1k Dec 30, 2022
Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction on how to make use of the S

RISHABH MISHRA 1 Feb 13, 2022
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing ProFuzzBench is a benchmark for stateful fuzzing of network protocols. It includes a suite of

155 Jan 08, 2023
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion

Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will

InterDigital 63 Jan 03, 2023
Model Quantization Benchmark

Introduction MQBench is an open-source model quantization toolkit based on PyTorch fx. The envision of MQBench is to provide: SOTA Algorithms. With MQ

500 Jan 06, 2023
Retina blood vessel segmentation with a convolutional neural network

Retina blood vessel segmentation with a convolution neural network (U-net) This repository contains the implementation of a convolutional neural netwo

Orobix 1.2k Jan 06, 2023
PyContinual (An Easy and Extendible Framework for Continual Learning)

PyContinual (An Easy and Extendible Framework for Continual Learning) Easy to Use You can sumply change the baseline, backbone and task, and then read

176 Jan 05, 2023
Automatic Image Background Subtraction

Automatic Image Background Subtraction This repo contains set of scripts for automatic one-shot image background subtraction task using the following

Oleg Sémery 6 Dec 05, 2022
The Pytorch implementation for "Video-Text Pre-training with Learned Regions"

Region_Learner The Pytorch implementation for "Video-Text Pre-training with Learned Regions" (arxiv) We are still cleaning up the code further and pre

Rui Yan 0 Mar 20, 2022
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

Learning to Classify Images without Labels This repo contains the Pytorch implementation of our paper: SCAN: Learning to Classify Images without Label

Wouter Van Gansbeke 1.1k Dec 30, 2022
Official code of paper: MovingFashion: a Benchmark for the Video-to-Shop Challenge

SEAM Match-RCNN Official code of MovingFashion: a Benchmark for the Video-to-Shop Challenge paper Installation Requirements: Pytorch 1.5.1 or more rec

HumaticsLAB 31 Oct 10, 2022
This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

0 Feb 02, 2022
DeepGNN is a framework for training machine learning models on large scale graph data.

DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in

Microsoft 45 Jan 01, 2023
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
This is a project based on ConvNets used to identify whether a road is clean or dirty. We have used MobileNet as our base architecture and the weights are based on imagenet.

PROJECT TITLE: CLEAN/DIRTY ROAD DETECTION USING TRANSFER LEARNING Description: This is a project based on ConvNets used to identify whether a road is

Faizal Karim 3 Nov 06, 2022
Tensorflow/Keras Plug-N-Play Deep Learning Models Compilation

DeepBay This project was created with the objective of compile Machine Learning Architectures created using Tensorflow or Keras. The architectures mus

Whitman Bohorquez 4 Sep 26, 2022
PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks

AttentionHTR PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks. Scene Text

Dmitrijs Kass 31 Dec 22, 2022