code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

Related tags

Deep LearningMMNet
Overview

MMNet

This repo is the official implementation of ICCV 2021 paper "Multi-scale Matching Networks for Semantic Correspondence.".

Pre-requisite

conda create -n mmnet python==3.8.0
conda activate mmnet
conda install torch==1.8.1 torchvision==0.9.1
pip install matplotlib scikit-image pandas

for installation of gluoncvth (fcn-resnet101):

git clone https://github.com/StacyYang/gluoncv-torch.git
cd gluoncv-torch
python setup.py install

Reproduction

for test

Trained models are available on [google drive].

pascal with fcn-resnet101 backbone([email protected]:81.6%):

python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name path\to\ckp_pascal_fcnres101.pth --resize 224,320

spair with fcn-resnet101 backbone([email protected]:46.6%):

python test.py --alpha 0.05 --benchmark spair --backbone fcn-resnet101 --ckp_name path\to\ckp_spair_fcnres101.pth --resize 224,320

Bibtex

If you use this code for your research, please consider citing:

@article{zhao2021multi,
  title={Multi-scale Matching Networks for Semantic Correspondence},
  author={Zhao, Dongyang and Song, Ziyang and Ji, Zhenghao and Zhao, Gangming and Ge, Weifeng and Yu, Yizhou},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}
You might also like...
A Pytorch implementation of
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

null 69 Dec 10, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 26 Sep 26, 2021
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 4, 2023
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Siamese Deep Neural Networks for Semantic Text Similarity PyTorch A repository c

Shahrukh Khan 32 Dec 15, 2022
A PyTorch implementation of
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 2, 2023
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 4, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
《Dual-Resolution Correspondence Network》(NeurIPS 2020)
《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Dual-Resolution Correspondence Network Dual-Resolution Correspondence Network, NeurIPS 2020 Dependency All dependencies are included in asset/dualrcne

Active Vision Laboratory 45 Nov 21, 2022
Comments
  • NaN during training

    NaN during training

    Hi, congrats on your paper! I was trying to run your training code (with resnet 101 on pf-pascal) but directly after a couple of iterations, nan appear in the input. Have you ever seen this issue? Thanks

    opened by PruneTruong 2
  • In def calLayer1,i do not know where are self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1

    In def calLayer1,i do not know where are self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1

    Hello,this paper is very nice,i am very love it. I read your code,in Model.py, def calLayer1(self, feats): sum1 = self.conv1_1_down(self.msblock1_1(feats[1])) +
    self.conv1_2_down(self.msblock1_2(feats[2])) +
    self.conv1_3_down(self.msblock1_3(feats[3])) sum1 = self.wa_1(sum1) return sum1 I do not find where are these operation,self.conv1_1_down,self.conv1_2_down,self.conv1_3_down,self.wa_1,so where are these ,in which document.Thank you,looking forward to your reply.

    opened by liang532 1
  • How to prepare the PF-Pascal dataset?

    How to prepare the PF-Pascal dataset?

    I downloaded the PF-dataset-Pascal.zip from the Proposal Flow paper's web page, extracted it, and run the next line of command, but get errors about missing data files.

    Input:

    python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name assets/model/mmnet_fcnresnet101_pascal.pth --resize 224,320

    Expected output: some results about the benchmark results.

    Actual output:

    currently executing test.py file.
2021-11-19 02:01:59,172 - INFO - Options listed below:----------------
2021-11-19 02:01:59,172 - INFO - name: framework_train
2021-11-19 02:01:59,172 - INFO - benchmark: pfpascal
2021-11-19 02:01:59,172 - INFO - thresh_type: auto
2021-11-19 02:01:59,172 - INFO - backbone_name: fcn-resnet101
2021-11-19 02:01:59,172 - INFO - ms_rate: 4
2021-11-19 02:01:59,173 - INFO - feature_channel: 21
2021-11-19 02:01:59,173 - INFO - batch: 5
2021-11-19 02:01:59,173 - INFO - gpu: 0
2021-11-19 02:01:59,173 - INFO - data_path: /data/SC_Dataset
2021-11-19 02:01:59,173 - INFO - ckp_path: ./checkpoints_debug
2021-11-19 02:01:59,173 - INFO - visualization_path: visualization
2021-11-19 02:01:59,173 - INFO - model_type: MMNet
2021-11-19 02:01:59,173 - INFO - ckp_name: assets/model/mmnet_fcnresnet101_pascal.pth
2021-11-19 02:01:59,173 - INFO - log_path: ./logs/
2021-11-19 02:01:59,173 - INFO - resize: 224,320
2021-11-19 02:01:59,173 - INFO - max_kps_num: 50
2021-11-19 02:01:59,173 - INFO - split_type: test
2021-11-19 02:01:59,173 - INFO - alpha: 0.05
2021-11-19 02:01:59,173 - INFO - resolution: 2
2021-11-19 02:01:59,173 - INFO - Options all listed.------------------
2021-11-19 02:01:59,173 - INFO - ckp file: assets/model/mmnet_fcnresnet101_pascal.pth
Traceback (most recent call last):
  File "/home/runner/MMNet/test.py", line 127, in <module>
    test(logger, options)
  File "/home/runner/MMNet/test.py", line 65, in test
    test_dataset = Dataset.CorrespondenceDataset(
  File "/home/runner/MMNet/data/PascalDataset.py", line 32, in __init__
    self.train_data = pd.read_csv(self.spt_path)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/util/_decorators.py", line 311, in wrapper
    return func(*args, **kwargs)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
    return _read(filepath_or_buffer, kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 482, in _read
    parser = TextFileReader(filepath_or_buffer, **kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 811, in __init__
    self._engine = self._make_engine(self.engine)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1040, in _make_engine
    return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 51, in __init__
    self._open_handles(src, kwds)
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py", line 222, in _open_handles
    self.handles = get_handle(
  File "/home/runner/miniconda3/lib/python3.9/site-packages/pandas/io/common.py", line 702, in get_handle
    handle = open(
FileNotFoundError: [Errno 2] No such file or directory: '/data/SC_Dataset/PF-PASCAL/test_pairs.csv'

    P.S. Output of executing ls /data/SC_Dataset/PF-PASCAL/:

    Annotations  html  index.html  JPEGImages  parsePascalVOC.mat  ShowMatchingPairs
    opened by tjyuyao 2
  • How to reproduce the reported test accuracy?

    How to reproduce the reported test accuracy?

    By running given following command with code on the main branch:

    python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name assets/model/mmnet_fcnresnet101_spair.pth --resize 224,320 --benchmark spair

    I expect to get the reported accuracy in the Table.2 of paper, i.e. 50.4 "all" accuracy, or spair with fcn-resnet101 backbone([email protected]:46.6%): as noted in the README.md file. However I get the following output, finding nowhere the related results. Can you point out the steps to reproduce the test accuracy?

    2021-11-19 00:49:54,452 - INFO - Options listed below:----------------
2021-11-19 00:49:54,452 - INFO - name: framework_train
2021-11-19 00:49:54,453 - INFO - benchmark: spair
2021-11-19 00:49:54,453 - INFO - thresh_type: auto
2021-11-19 00:49:54,454 - INFO - backbone_name: fcn-resnet101
2021-11-19 00:49:54,455 - INFO - ms_rate: 4
2021-11-19 00:49:54,455 - INFO - feature_channel: 21
2021-11-19 00:49:54,456 - INFO - batch: 5
2021-11-19 00:49:54,456 - INFO - gpu: 0
2021-11-19 00:49:54,457 - INFO - data_path: /data/SC_Dataset
2021-11-19 00:49:54,457 - INFO - ckp_path: ./checkpoints_debug
2021-11-19 00:49:54,458 - INFO - visualization_path: visualization
2021-11-19 00:49:54,458 - INFO - model_type: MMNet
2021-11-19 00:49:54,459 - INFO - ckp_name: assets/model/mmnet_fcnresnet101_spair.pth
2021-11-19 00:49:54,459 - INFO - log_path: ./logs/
2021-11-19 00:49:54,460 - INFO - resize: 224,320
2021-11-19 00:49:54,460 - INFO - max_kps_num: 50
2021-11-19 00:49:54,461 - INFO - split_type: test
2021-11-19 00:49:54,461 - INFO - alpha: 0.05
2021-11-19 00:49:54,462 - INFO - resolution: 2
2021-11-19 00:49:54,462 - INFO - Options all listed.------------------
2021-11-19 00:49:54,463 - INFO - ckp file: assets/model/mmnet_fcnresnet101_spair.pth
2021-11-19 00:50:04,950 - INFO - [    0/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] aeroplane
2021-11-19 00:50:04,953 - INFO - [    1/12234]: 	 [Pair PCK: 0.100]	[Average: 0.217] aeroplane
2021-11-19 00:50:04,956 - INFO - [    2/12234]: 	 [Pair PCK: 0.308]	[Average: 0.247] aeroplane
2021-11-19 00:50:04,958 - INFO - [    3/12234]: 	 [Pair PCK: 0.364]	[Average: 0.276] aeroplane
2021-11-19 00:50:04,960 - INFO - [    4/12234]: 	 [Pair PCK: 0.000]	[Average: 0.221] aeroplane
2021-11-19 00:50:05,575 - INFO - [    5/12234]: 	 [Pair PCK: 0.200]	[Average: 0.217] aeroplane
2021-11-19 00:50:05,577 - INFO - [    6/12234]: 	 [Pair PCK: 0.250]	[Average: 0.222] aeroplane
2021-11-19 00:50:05,580 - INFO - [    7/12234]: 	 [Pair PCK: 0.308]	[Average: 0.233] aeroplane
2021-11-19 00:50:05,583 - INFO - [    8/12234]: 	 [Pair PCK: 0.182]	[Average: 0.227] aeroplane
2021-11-19 00:50:05,585 - INFO - [    9/12234]: 	 [Pair PCK: 0.636]	[Average: 0.268] aeroplane
2021-11-19 00:50:06,153 - INFO - [   10/12234]: 	 [Pair PCK: 0.667]	[Average: 0.304] aeroplane
2021-11-19 00:50:06,156 - INFO - [   11/12234]: 	 [Pair PCK: 0.385]	[Average: 0.311] aeroplane
2021-11-19 00:50:06,158 - INFO - [   12/12234]: 	 [Pair PCK: 0.455]	[Average: 0.322] aeroplane
2021-11-19 00:50:06,160 - INFO - [   13/12234]: 	 [Pair PCK: 0.250]	[Average: 0.317] aeroplane
2021-11-19 00:50:06,163 - INFO - [   14/12234]: 	 [Pair PCK: 0.615]	[Average: 0.337] aeroplane
2021-11-19 00:50:06,731 - INFO - [   15/12234]: 	 [Pair PCK: 0.000]	[Average: 0.316] aeroplane
...
2021-11-19 01:13:47,264 - INFO - [12216/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,265 - INFO - [12217/12234]: 	 [Pair PCK: 0.200]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,266 - INFO - [12218/12234]: 	 [Pair PCK: 0.250]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,268 - INFO - [12219/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,837 - INFO - [12220/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,838 - INFO - [12221/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,848 - INFO - [12222/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,850 - INFO - [12223/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:47,853 - INFO - [12224/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,422 - INFO - [12225/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,424 - INFO - [12226/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,425 - INFO - [12227/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,427 - INFO - [12228/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,429 - INFO - [12229/12234]: 	 [Pair PCK: 0.222]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,896 - INFO - [12230/12234]: 	 [Pair PCK: 0.333]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,899 - INFO - [12231/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,899 - INFO - [12232/12234]: 	 [Pair PCK: 0.000]	[Average: 0.333] tvmonitor
2021-11-19 01:13:48,901 - INFO - [12233/12234]: 	 [Pair PCK: 0.111]	[Average: 0.333] tvmonitor
    opened by tjyuyao 1
Releases(v0.1.0)
Owner
joey zhao
Master in Computer Sciences and Technology at Fudan University
joey zhao
GitHub Repository
Uses OpenCV and Python Code to detect a face on the screen

Simple-Face-Detection This code uses OpenCV and Python Code to detect a face on the screen. This serves as an example program. Important prerequisites

Denis Woolley (CreepyD) 1 Feb 12, 2022
This is the official source code of "BiCAT: Bi-Chronological Augmentation of Transformer for Sequential Recommendation".

BiCAT This is our TensorFlow implementation for the paper: "BiCAT: Sequential Recommendation with Bidirectional Chronological Augmentation of Transfor

John 15 Dec 06, 2022
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

RuiLiu 65 Dec 20, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Realtime_Multi-Person_Pose_Estimation

Introduction Multi Person PoseEstimation By PyTorch Results Require Pytorch Installation git submodule init && git submodule update Demo Download conv

tensorboy 1.3k Jan 05, 2023
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

SAFL: A Self-Attention Scene Text Recognizer with Focal Loss This repository implements the SAFL in pytorch. Installation conda env create -f environm

6 Aug 24, 2022
Code for "Human Pose Regression with Residual Log-likelihood Estimation", ICCV 2021 Oral

Human Pose Regression with Residual Log-likelihood Estimation [Paper] [arXiv] [Project Page] Human Pose Regression with Residual Log-likelihood Estima

JeffLi 347 Dec 24, 2022
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021
This repository contains a pytorch implementation of "StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision".

StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision | Project Page | Paper | This repository contains a pytorch implementation of "St

87 Dec 09, 2022
✂️ EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video.

EyeLipCropper EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video. The whole process consists of three parts: frame extracti

Zi-Han Liu 9 Oct 25, 2022
Tensorflow implementation of Character-Aware Neural Language Models.

Character-Aware Neural Language Models Tensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found h

Taehoon Kim 751 Dec 26, 2022
Trajectory Extraction of road users via Traffic Camera

Traffic Monitoring Citation The associated paper for this project will be published here as soon as possible. When using this software, please cite th

Julian Strosahl 14 Dec 17, 2022
Super Resolution for images using deep learning.

Neural Enhance Example #1 — Old Station: view comparison in 24-bit HD, original photo CC-BY-SA @siv-athens. As seen on TV! What if you could increase

Alex J. Champandard 11.7k Dec 29, 2022
Deep Sea Treasure Environment for Multi-Objective Optimization Research

DeepSeaTreasure Environment Installation In order to get started with this environment, you can install it using the following command: python3 -m pip

imec IDLab 6 Nov 14, 2022
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 03, 2023
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in C

Yulei Niu 94 Dec 03, 2022
PyTorch implementation of the WarpedGANSpace: Finding non-linear RBF paths in GAN latent space (ICCV 2021)

Authors official PyTorch implementation of the "WarpedGANSpace: Finding non-linear RBF paths in GAN latent space" [ICCV 2021].

Christos Tzelepis 100 Dec 06, 2022
A PyTorch Implementation of SphereFace.

SphereFace A PyTorch Implementation of SphereFace. The code can be trained on CASIA-Webface and the best accuracy on LFW is 99.22%. SphereFace: Deep H

carwin 685 Dec 09, 2022
D2Go is a toolkit for efficient deep learning

D2Go D2Go is a production ready software system from FacebookResearch, which supports end-to-end model training and deployment for mobile platforms. W

Facebook Research 744 Jan 04, 2023
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti

Zhuang AI Group 77 Dec 26, 2022