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
DvD-TD3: Diversity via Determinants for TD3 version

DvD-TD3: Diversity via Determinants for TD3 version The implementation of paper Effective Diversity in Population Based Reinforcement Learning. Instal

3 Feb 11, 2022
Implementation of the paper titled "Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees"

Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees Implementation of the paper titled "Using Sampling to

MIDAS, IIIT Delhi 2 Aug 29, 2022
Python project to take sound as input and output as RGB + Brightness values suitable for DMX

sound-to-light Python project to take sound as input and output as RGB + Brightness values suitable for DMX Current goals: Get one pixel working: Vary

Bobby Cox 1 Nov 17, 2021
Multi-Modal Machine Learning toolkit based on PaddlePaddle.

简体中文 | English PaddleMM 简介 飞桨多模态学习工具包 PaddleMM 旨在于提供模态联合学习和跨模态学习算法模型库,为处理图片文本等多模态数据提供高效的解决方案,助力多模态学习应用落地。 近期更新 2022.1.5 发布 PaddleMM 初始版本 v1.0 特性 丰富的任务

njustkmg 520 Dec 28, 2022
The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines. It includes tools for downloading pipelines and their dependencies and tools for measuring their performace

8 Dec 04, 2022
Toward Multimodal Image-to-Image Translation

BicycleGAN Project Page | Paper | Video Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our

Jun-Yan Zhu 1.4k Dec 22, 2022
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

SciKit-Learn Laboratory This Python package provides command-line utilities to make it easier to run machine learning experiments with scikit-learn. O

ETS 528 Nov 25, 2022
Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight

Second-order Neural ODE Optimizer (NeurIPS 2021 Spotlight) [arXiv] ✔️ faster convergence in wall-clock time | ✔️ O(1) memory cost | ✔️ better test-tim

Guan-Horng Liu 39 Oct 22, 2022
Permute Me Softly: Learning Soft Permutations for Graph Representations

Permute Me Softly: Learning Soft Permutations for Graph Representations

Giannis Nikolentzos 7 Jul 10, 2022
KaziText is a tool for modelling common human errors.

KaziText KaziText is a tool for modelling common human errors. It estimates probabilities of individual error types (so called aspects) from grammatic

ÚFAL 3 Nov 24, 2022
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting.

FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu

77 Dec 27, 2022
This repo contains code to reproduce all experiments in Equivariant Neural Rendering

Equivariant Neural Rendering This repo contains code to reproduce all experiments in Equivariant Neural Rendering by E. Dupont, M. A. Bautista, A. Col

Apple 83 Nov 16, 2022
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

DocFormer - PyTorch Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for t

171 Jan 06, 2023
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

Oliver Hahn 1 Jan 26, 2022
Repository for the AugmentedPCA Python package.

Overview This Python package provides implementations of Augmented Principal Component Analysis (AugmentedPCA) - a family of linear factor models that

Billy Carson 6 Dec 07, 2022
Open-source implementation of Google Vizier for hyper parameters tuning

Advisor Introduction Advisor is the hyper parameters tuning system for black box optimization. It is the open-source implementation of Google Vizier w

tobe 1.5k Jan 04, 2023
Official repository for Few-shot Image Generation via Cross-domain Correspondence (CVPR '21)

Few-shot Image Generation via Cross-domain Correspondence Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zh

Utkarsh Ojha 251 Dec 11, 2022
Si Adek Keras is software VR dangerous object detection.

Si Adek Python Keras Sistem Informasi Deteksi Benda Berbahaya Keras Python. Version 1.0 Developed by Ananda Rauf Maududi. Developed date: 24 November

Ananda Rauf 1 Dec 21, 2021