PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

Overview

Asym-Siam: On the Importance of Asymmetry for Siamese Representation Learning

This is a PyTorch implementation of the Asym-Siam paper, CVPR 2022:

@inproceedings{wang2022asym,
  title     = {On the Importance of Asymmetry for Siamese Representation Learning},
  author    = {Xiao Wang and Haoqi Fan and Yuandong Tian and Daisuke Kihara and Xinlei Chen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022}
}

The pre-training code is built on MoCo, with additional designs described and analyzed in the paper.

The linear classification code is from SimSiam, which uses LARS optimizer.

Installation

  1. Install git

  2. Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code.

  3. Install apex for the LARS optimizer used in linear classification. If you find it hard to install apex, it suffices to just copy the code directly for use.

  4. Clone the repository:

git clone https://github.com/facebookresearch/asym-siam & cd asym-siam

1 Unsupervised Training

This implementation only supports multi-gpu, DistributedDataParallel training, which is faster and simpler; single-gpu or DataParallel training is not supported.

1.1 Our MoCo Baseline (BN in projector MLP)

To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run:

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

This script uses all the default hyper-parameters as described in the MoCo v2 paper. We only upgrade the projector to a MLP with BN layer.

1.2 MoCo + MultiCrop

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders] --enable-multicrop

By simply setting --enable-multicrop to true, we can have asym MultiCrop on source side.

1.3 MoCo + ScaleMix

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders] --enable-scalemix

By simply setting --enable-scalemix to true, we can have asym ScaleMix on source side.

1.4 MoCo + AsymAug

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders] --enable-asymm-aug

By simply setting --enable-asymm-aug to true, we can have Stronger Augmentation on source side and Weaker Augmentation on target side.

1.5 MoCo + AsymBN

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders] --enable-asym-bn

By simply setting --enable-asym-bn to true, we can have asym BN on target side (sync BN for target).

1.6 MoCo + MeanEnc

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders] --enable-mean-encoding

By simply setting --enable-mean-encoding to true, we can have MeanEnc on target side.

2 Linear Classification

With a pre-trained model, to train a supervised linear classifier on frozen features/weights, run:

python main_lincls.py \
  -a resnet50 \
  --lars \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  --pretrained [your checkpoint path] \
  [your imagenet-folder with train and val folders]

Linear classification results on ImageNet using this repo with 8 NVIDIA V100 GPUs :

Method pre-train
epochs
pre-train
time
top-1 model md5
Our MoCo 100 23.6h 65.8 download e82ede
MoCo
+MultiCrop
100 50.8h 69.9 download 892916
MoCo
+ScaleMix
100 30.7h 67.6 download 3f5d79
MoCo
+AsymAug
100 24.0h 67.2 download d94e24
MoCo
+AsymBN
100 23.8h 66.3 download 2bf912
MoCo
+MeanEnc
100 32.2h 67.7 download 599801

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

Owner
Meta Research
Meta Research
pytorch implementation of the ICCV'21 paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition"

MVTN: Multi-View Transformation Network for 3D Shape Recognition (ICCV 2021) By Abdullah Hamdi, Silvio Giancola, Bernard Ghanem Paper | Video | Tutori

Abdullah Hamdi 64 Jan 03, 2023
Virtual Dance Reality Stage: a feature that offers you to share a stage with another user virtually

Portrait Segmentation using Tensorflow This script removes the background from an input image. You can read more about segmentation here Setup The scr

291 Dec 24, 2022
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
pytorch implementation of GPV-Pose

GPV-Pose Pytorch implementation of GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting. (link) UPDATE A new version

40 Dec 01, 2022
Affine / perspective transformation in Pose Estimation with Tensorflow 2

Pose Transformation Affine / Perspective transformation in Pose Estimation with Tensorflow 2 Introduction 이 repo는 pose estimation을 연구하고 개발하는 데 도움이 되기

Kim Junho 1 Dec 22, 2021
A framework for annotating 3D meshes using the predictions of a 2D semantic segmentation model.

Semantic Meshes A framework for annotating 3D meshes using the predictions of a 2D semantic segmentation model. Paper If you find this framework usefu

Florian 40 Dec 09, 2022
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022
Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023
2021 credit card consuming recommendation

2021 credit card consuming recommendation

Wang, Chung-Che 7 Mar 08, 2022
The world's largest toxicity dataset.

The Toxicity Dataset by Surge AI Saving the internet is fun. Combing through thousands of online comments to build a toxicity dataset isn't. That's wh

Surge AI 134 Dec 19, 2022
Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485

python-pylontech Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485 What is this lib ? This lib is meant to talk to P

Frank 26 Dec 28, 2022
Perturb-and-max-product: Sampling and learning in discrete energy-based models

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

Vicarious 2 Mar 14, 2022
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rend

Jonáš Kulhánek 169 Dec 30, 2022
Image Restoration Using Swin Transformer for VapourSynth

SwinIR SwinIR function for VapourSynth, based on https://github.com/JingyunLiang/SwinIR. Dependencies NumPy PyTorch, preferably with CUDA. Note that t

Holy Wu 11 Jun 19, 2022
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition The official code of ABINet (CVPR 2021, Oral).

334 Dec 31, 2022
An implementation of the WHATWG URL Standard in JavaScript

whatwg-url whatwg-url is a full implementation of the WHATWG URL Standard. It can be used standalone, but it also exposes a lot of the internal algori

314 Dec 28, 2022
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal We propose a new method for effective shadow removal by regarding it as an exposure fusion proble

Qing Guo 146 Dec 31, 2022
quantize aware training package for NCNN on pytorch

ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples

62 Nov 23, 2022
Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

Decoupled Spatial-Temporal Transformer for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, J

51 Dec 13, 2022
This repo contains research materials released by members of the Google Brain team in Tokyo.

Brain Tokyo Workshop 🧠 🗼 This repo contains research materials released by members of the Google Brain team in Tokyo. Past Projects Weight Agnostic

Google 1.2k Jan 02, 2023