Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021

Related tags

Deep Learningunsupervised-learningcontrastive-learningpixprocvpr2021pixel-level-contrastive-learning
Overview

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

By Zhenda Xie*, Yutong Lin*, Zheng Zhang, Yue Cao, Stephen Lin and Han Hu.

This repo is an official implementation of "Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning" on PyTorch.

Introduction

PixPro (pixel-to-propagation) is an unsupervised visual feature learning approach by leveraging pixel-level pretext tasks. The learnt feature can be well transferred to downstream dense prediction tasks such as object detection and semantic segmentation. PixPro achieves the best transferring performance on Pascal VOC object detection (60.2 AP using C4) and COCO object detection (41.4 / 40.5 mAP using FPN / C4) with a ResNet-50 backbone.

An illustration of the proposed PixPro method.

Architecture of the PixContrast and PixPro methods.

Citation

@article{xie2020propagate,
  title={Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning},
  author={Xie, Zhenda and Lin, Yutong and Zhang, Zheng and Cao, Yue and Lin, Stephen and Hu, Han},
  conference={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

Main Results

PixPro pre-trained models

Epochs Arch Instance Branch Download
100 ResNet-50 script | model
400 ResNet-50 script | model
100 ResNet-50 ✔️ -
400 ResNet-50 ✔️ -

Pascal VOC object detection

Faster-RCNN with C4

Method Epochs Arch AP AP50 AP75 Download
Scratch - ResNet-50 33.8 60.2 33.1 -
Supervised 100 ResNet-50 53.5 81.3 58.8 -
MoCo 200 ResNet-50 55.9 81.5 62.6 -
SimCLR 1000 ResNet-50 56.3 81.9 62.5 -
MoCo v2 800 ResNet-50 57.6 82.7 64.4 -
InfoMin 200 ResNet-50 57.6 82.7 64.6 -
InfoMin 800 ResNet-50 57.5 82.5 64.0 -
PixPro (ours) 100 ResNet-50 58.8 83.0 66.5 config | model
PixPro (ours) 400 ResNet-50 60.2 83.8 67.7 config | model

COCO object detection

Mask-RCNN with FPN

Method Epochs Arch Schedule bbox AP mask AP Download
Scratch - ResNet-50 1x 32.8 29.9 -
Supervised 100 ResNet-50 1x 39.7 35.9 -
MoCo 200 ResNet-50 1x 39.4 35.6 -
SimCLR 1000 ResNet-50 1x 39.8 35.9 -
MoCo v2 800 ResNet-50 1x 40.4 36.4 -
InfoMin 200 ResNet-50 1x 40.6 36.7 -
InfoMin 800 ResNet-50 1x 40.4 36.6 -
PixPro (ours) 100 ResNet-50 1x 40.8 36.8 config | model
PixPro (ours) 100* ResNet-50 1x 41.3 37.1 -
PixPro (ours) 400* ResNet-50 1x 41.4 37.4 -

* Indicates methods with instance branch.

Mask-RCNN with C4

Method Epochs Arch Schedule bbox AP mask AP Download
Scratch - ResNet-50 1x 26.4 29.3 -
Supervised 100 ResNet-50 1x 38.2 33.3 -
MoCo 200 ResNet-50 1x 38.5 33.6 -
SimCLR 1000 ResNet-50 1x 38.4 33.6 -
MoCo v2 800 ResNet-50 1x 39.5 34.5 -
InfoMin 200 ResNet-50 1x 39.0 34.1 -
InfoMin 800 ResNet-50 1x 38.8 33.8 -
PixPro (ours) 100 ResNet-50 1x 40.0 34.8 config | model
PixPro (ours) 400 ResNet-50 1x 40.5 35.3 config | model

Getting started

Requirements

At present, we have not checked the compatibility of the code with other versions of the packages, so we only recommend the following configuration.

  • Python 3.7
  • PyTorch == 1.4.0
  • Torchvision == 0.5.0
  • CUDA == 10.1
  • Other dependencies

Installation

We recommand using conda env to setup the experimental environments.

# Create environment
conda create -n PixPro python=3.7 -y
conda activate PixPro

# Install PyTorch & Torchvision
conda install pytorch=1.4.0 cudatoolkit=10.1 torchvision -c pytorch -y

# Install apex
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ..

# Clone repo
git clone https://github.com/zdaxie/PixPro ./PixPro
cd ./PixPro

# Create soft link for data
mkdir data
ln -s ${ImageNet-Path} ./data/imagenet

# Install other requirements
pip install -r requirements.txt

Pretrain with PixPro

# Train with PixPro base for 100 epochs.
./tools/pixpro_base_r50_100ep.sh

Transfer to Pascal VOC or COCO object detection

# Convert a pre-trained PixPro model to detectron2's format
cd transfer/detection
python convert_pretrain_to_d2.py ${Input-Checkpoint(.pth)} ./output.pkl  

# Install Detectron2
python -m pip install detectron2==0.2.1 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.4/index.html

# Create soft link for data
mkdir datasets
ln -s ${Pascal-VOC-Path}/VOC2007 ./datasets/VOC2007
ln -s ${Pascal-VOC-Path}/VOC2012 ./datasets/VOC2012
ln -s ${COCO-Path} ./datasets/coco

# Train detector with pre-trained PixPro model
# 1. Train Faster-RCNN with Pascal-VOC
python train_net.py --config-file configs/Pascal_VOC_R_50_C4_24k_PixPro.yaml --num-gpus 8 MODEL.WEIGHTS ./output.pkl
# 2. Train Mask-RCNN-FPN with COCO
python train_net.py --config-file configs/COCO_R_50_FPN_1x_PixPro.yaml --num-gpus 8 MODEL.WEIGHTS ./output.pkl
# 3. Train Mask-RCNN-C4 with COCO
python train_net.py --config-file configs/COCO_R_50_C4_1x_PixPro.yaml --num-gpus 8 MODEL.WEIGHTS ./output.pkl

# Test detector with provided fine-tuned model
python train_net.py --config-file configs/Pascal_VOC_R_50_C4_24k_PixPro.yaml --num-gpus 8 --eval-only \
  MODEL.WEIGHTS ./pixpro_base_r50_100ep_voc_md5_ec2dfa63.pth

More models and logs will be released!

Acknowledgement

Our testbed builds upon several existing publicly available codes. Specifically, we have modified and integrated the following code into this project:

Contributing to the project

Any pull requests or issues are welcomed.

Owner
Zhenda Xie
Zhenda Xie
GitHub Repository https://arxiv.org/abs/2011.10043
Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them

TensorFlow Serving + Streamlit! ✨ 🖼️ Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them! This is a pretty simple S

Álvaro Bartolomé 18 Jan 07, 2023
git《Joint Entity and Relation Extraction with Set Prediction Networks》(2020) GitHub:

Joint Entity and Relation Extraction with Set Prediction Networks Source code for Joint Entity and Relation Extraction with Set Prediction Networks. W

130 Dec 13, 2022
Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

18 May 27, 2022
Pure python implementation reverse-mode automatic differentiation

MiniGrad A minimal implementation of reverse-mode automatic differentiation (a.k.a. autograd / backpropagation) in pure Python. Inspired by Andrej Kar

Kenny Song 76 Sep 12, 2022
Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Codes-for-Algorithms Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Tracy (Shengmin) Tao 1 Apr 12, 2022
B-cos Networks: Attention is All we Need for Interpretability

Convolutional Dynamic Alignment Networks for Interpretable Classifications M. Böhle, M. Fritz, B. Schiele. B-cos Networks: Alignment is All we Need fo

58 Dec 23, 2022
LAnguage Model Analysis

LAMA: LAnguage Model Analysis LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models. The dataset

Meta Research 960 Jan 08, 2023
Deep Ensemble Learning with Jet-Like architecture

Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

Ahsen Nazir 2 Feb 06, 2022
Prediction of MBA refinance Index (Mortgage prepayment)

Prediction of MBA refinance Index (Mortgage prepayment) Deep Neural Network based Model The ability to predict mortgage prepayment is of critical use

Ruchil Barya 1 Jan 16, 2022
Source code for Fathony, Sahu, Willmott, & Kolter, "Multiplicative Filter Networks", ICLR 2021.

Multiplicative Filter Networks This repository contains a PyTorch MFN implementation and code to perform & reproduce experiments from the ICLR 2021 pa

Bosch Research 66 Jan 04, 2023
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

Ching-Yao Chuang 427 Dec 13, 2022
Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network [Paper Link] [Project Page] He Zhang, Vishwanath Sindagi, Vishal M. Patel In this

He Zhang 216 Dec 18, 2022
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 02, 2023
A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano

yolov5-helmet-detection-python A Python implementation of Yolov5 to detect head or helmet in the wild in Jetson Xavier nx and Jetson nano. In Jetson X

12 Dec 05, 2022
Erpnext app for make employee salary on payroll entry based on one or more project with percentage for all project equal 100 %

Project Payroll this app for make payroll for employee based on projects like project on 30 % and project 2 70 % as account dimension it makes genral

Ibrahim Morghim 8 Jan 02, 2023
NeuPy is a Tensorflow based python library for prototyping and building neural networks

NeuPy v0.8.2 NeuPy is a python library for prototyping and building neural networks. NeuPy uses Tensorflow as a computational backend for deep learnin

Yurii Shevchuk 729 Jan 03, 2023
No Code AI/ML platform

NoCodeAIML No Code AI/ML platform - Community Edition Video credits: Uday Kiran Typical No Code AI/ML Platform will have features like drag and drop,

Bhagvan Kommadi 5 Jan 28, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT ***************New March 28, 2020 *************** Add a colab tutorial to run fine-tuning for GLUE datasets. ***************New January 7, 2020

Google Research 3k Jan 01, 2023