PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

Overview

PatchGame: Learning to Signal Mid-level Patches in Referential Games

This repository is the official implementation of the paper - "PatchGame: Learning to SignalMid-level Patches in Referential Games"

Overview

Requirements

We recommend using anaconda or miniconda for python. Our code has been tested with python=3.8 on linux.

To create a new environment with conda

conda create -n patchgame python=3.8
conda activate patchgame

We recommend installing the latest pytorch and torchvision packages You can install them using

conda install pytorch torchvision -c pytorch

Make sure the following requirements are met

  • torch>=1.8.1
  • torchvision>=0.9.1

Installing torchsort

Note we only tried installing torchsort with following cuda==10.2.89 and gcc==6.3.0.

export TORCH_CUDA_ARCH_LIST="Pascal;Volta;Turing"
unzip torchsort.zip && cd torchsort
python setup.py install --user
cd .. && rm -rf torchsort

Dataset

We use ImageNet-1k (ILSVRC2012) data in all our experiments. Please download and save the data from the official website.

Training

To train the model(s) in the paper on 1-8 GPUs, run this command (where nproc_per_node is the number of gpus):

python -m torch.distributed.launch --nproc_per_node=1 train.py \
    --data_path /patch/to/imagenet/dir/train \
    --output_dir /path/to/checkpoint/dir \
    --patch_size 32 --epochs 100

Pre-trained Models

You can download pretrained models here trained on ImageNet using parameters using above command (and default hyperparameters).

Evaluation

PatchRank with ViT

python eval_patchrank.py --patch-model mymodel.pth --data-path <path to dataset> --topk <no. of patches to use>

This achieves the following accuracy on ImageNet.

Model name Top 1 Accuracy Top 5 Accuracy
PatchGame(S=32, topk=75, size=384x384) 58.4% 80.9%

k-NN classification ImageNet with listener's vision module

python -m torch.distributed.launch --nproc_per_node=1 eval_knn.py \
    --pretrained_weights /path/to/checkpoint/dir/checkpoint.pth \
    --arch resnet18 --nb_knn 20 \
    --batch_size_per_gpu 1024 --use_cuda 0 \
    --data_path /patch/to/imagenet/dir

This achieves the following accuracy on ImageNet

Model name Top 1 Accuracy Top 5 Accuracy
PatchGame(S=32) 30.3% 49.9%

Acknowledgements

We would like to thank several public repos from where we borrowed various utilities

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

🎃 Core identification module of AI powerful point reading system platform.

ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con

CrashKing 1 Jan 11, 2022
Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

567 Dec 26, 2022
Record radiologists' eye gaze when they are labeling images.

Record radiologists' eye gaze when they are labeling images. Read for installation, usage, and deep learning examples. Why use MicEye Versatile As a l

24 Nov 03, 2022
Learning Open-World Object Proposals without Learning to Classify

Learning Open-World Object Proposals without Learning to Classify Pytorch implementation for "Learning Open-World Object Proposals without Learning to

Dahun Kim 149 Dec 22, 2022
Improving Object Detection by Label Assignment Distillation

Improving Object Detection by Label Assignment Distillation This is the official implementation of the WACV 2022 paper Improving Object Detection by L

Cybercore Co. Ltd 51 Dec 08, 2022
A library of multi-agent reinforcement learning components and systems

Mava: a research framework for distributed multi-agent reinforcement learning Table of Contents Overview Getting Started Supported Environments System

InstaDeep Ltd 463 Dec 23, 2022
Application of the L2HMC algorithm to simulations in lattice QCD.

l2hmc-qcd 📊 Slides Recent talk on Training Topological Samplers for Lattice Gauge Theory from the Machine Learning for High Energy Physics, on and of

Sam Foreman 37 Dec 14, 2022
An implementation of RetinaNet in PyTorch.

RetinaNet An implementation of RetinaNet in PyTorch. Installation Training COCO 2017 Pascal VOC Custom Dataset Evaluation Todo Credits Installation In

Conner Vercellino 297 Jan 04, 2023
An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"

Channel LM Prompting (and beyond) This includes an original implementation of Sewon Min, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. "Noisy Cha

Sewon Min 92 Jan 07, 2023
Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)

Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) authors: Boris Knyazev, Michal Drozdzal, Graham Taylor, Adriana Romero-Soriano Overv

Facebook Research 462 Jan 03, 2023
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202

sangho.lee 28 Nov 08, 2022
A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.

GFNet-Pytorch (NeurIPS 2020) This repo contains the official code and pre-trained models for the glance and focus network (GFNet). Glance and Focus: a

Rainforest Wang 169 Oct 28, 2022
GDSC-ML Team Interview Task

GDSC-ML-Team---Interview-Task Task 1 : Clean or Messy room In this task we have to classify the given test images as clean or messy. - Link for datase

Aayush. 1 Jan 19, 2022
A library for implementing Decentralized Graph Neural Network algorithms.

decentralized-gnn A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. De

Multimedia Knowledge and Social Analytics Lab 5 Nov 07, 2022
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022
A modification of Daniel Russell's notebook merged with Katherine Crowson's hq-skip-net changes

Edits made to this repo by Katherine Crowson I have added several features to this repository for use in creating higher quality generative art (featu

Paul Fishwick 10 May 07, 2022
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Sky Computing Introduction Sky Computing is a load-balanced framework for federated learning model parallelism. It adaptively allocate model layers to

HPC-AI Tech 72 Dec 27, 2022
Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

AequeVox Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems README under development. Python Packages Required

Sai Sathiesh 2 Aug 28, 2022
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

SF-Net for fullband SE This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Ban

Guochen Yu 36 Dec 02, 2022