Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

Related tags

Deep Learning LPN
Overview

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

Python 3.6 License: MIT

LPN

[Paper]

NEWs

Prerequisites

  • Python 3.6
  • GPU Memory >= 8G
  • Numpy > 1.12.1
  • Pytorch 0.3+
  • scipy == 1.2.1
  • [Optional] apex (for float16) Requirements & Quick Start

Getting started

Dataset & Preparation

Download University-1652 upon request. You may use the request template.

Or download CVUSA / CVACT.

For CVUSA, I follow the training/test split in (https://github.com/Liumouliu/OriCNN).

Train & Evaluation

Train & Evaluation University-1652

sh run.sh

Train & Evaluation CVUSA

python prepare_cvusa.py  
sh run_cvusa.sh

Train & Evaluation CVACT

python prepare_cvact.py  
sh run_cvact.sh

Citation

@article{wang2021LPN,
  title={Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization},
  author={Wang, Tingyu and Zheng, Zhedong and Yan, Chenggang and Zhang, jiyong and Sun, Yaoqi and Zheng, Bolun and Yang, Yi},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2021},
  publisher={IEEE},
  note={doi:{
    \href{http://dx.doi.org/10.1109/TCSVT.2021.3061265}{10.1109/TCSVT.2021.3061265}}}
}
@article{zheng2020university,
  title={University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization},
  author={Zheng, Zhedong and Wei, Yunchao and Yang, Yi},
  journal={ACM Multimedia},
  year={2020}
}

Related Work

Comments
  • Question about the Classblock

    Question about the Classblock

    Hello, I've been reading this code recently. I have a problem that I don't understand.

    In the model.py file, the part_classifier function defined in the three_view_net function. During the training, the structure of the Classblock in this function is as follows: train

    During the test, the structure of classblock is as follows: test

    I want to ask, how is this achieved?

    opened by zkangkang0 2
  • CVUSA training exception

    CVUSA training exception

    hi,Thanks for your work! But when I was training CVUSA, the loss was always very large, and the accuracy rate was very low. I have processed the dataset according to prepare_cvusa.py

    -- Epoch 33/189


    -- train Loss: 122.1055 Satellite_Acc: 0.0339 Street_Acc: 0.0411

    -- Training complete in 450m 17s

    opened by Allen-lz 2
  • No such file or directory: './ACT_data.mat'

    No such file or directory: './ACT_data.mat'

    Hello, when I run the prepare_cvact.py file, I am prompted that "No such file or directory:'./ACT_data.mat'. I want to ask if this file is in the CVACT dataset? The dataset I downloaded does not contain this file.

    opened by zkangkang0 0
  • Directory structure of CVUSA dataset

    Directory structure of CVUSA dataset

    Hello, I downloaded the CVUSA dataset, but an error occurred when running prepare_ cvusa. py. There is no path in line 6 of the prepare_cvusa.py in my dataset. I want to know the directory structure of the CVUSA dataset to see if I downloaded the wrong dataset.

    opened by zkangkang0 0
  • about dataset cvusa

    about dataset cvusa

    hi,thank you for your great work. the data link mentioned in the article : http://cs.uky.edu/~jacobs/datasets/cvusa/ is broken, can you provide a new dirve link of cvusa dataset.

    Thank you and best regards.

    opened by IATFLG 0
  • Question about using Grad-CAM

    Question about using Grad-CAM

    Hello,

    Thank you for your great work.

    I was trying to use the draw_cam.py script to extract heatmap from results, but there were some errors occured during the experiment, and I do not really understand the problem here:

    Traceback (most recent call last): File "draw_cam.py", line 115, in draw_CAM(model, img_path, save_path, transform=data_transforms, visual_heatmap=False) File "draw_cam.py", line 42, in draw_CAM output.register_hook(extract) File "/.pyenv/versions/anaconda3-2021.05/lib/python3.8/site-packages/torch/_tensor.py", line 289, in register_hook raise RuntimeError("cannot register a hook on a tensor that " RuntimeError: cannot register a hook on a tensor that doesn't require gradient

    Could you check the script again and teach me how to solve this problem? Thank you and best regards.

    opened by viet2411 3
  • Questions for trainning on University-1652

    Questions for trainning on University-1652

    Hi,

    I directly trained the model to match images from two views (satellite -> drone) through the 'train.py', but the accuracy remained extremely low (from 0.0000 to 0.0060), and didn't grow with epoch increases.

    I changed some of the parameters for training on my computer, the changes are as follow: batchsize = 2 num_workers = 0 inputs2, labels2 are from the 'drone' directory LPN = True

    I wonder how I could achieve the accuracy. Are there any preparations upon the dataset needed before training? Thank you very much for your help!

    Zhaoxiang

    opened by nono-zz 2
  • Some Questions about SAFA

    Some Questions about SAFA

    you said you reimplemented the SAFA with Pytorch in your paper, but I only found the reference of SAFA in your code like these.

    image

    Could you give me the definition code of SAFA?

    opened by lsl1229840757 3
  • Some questions about the prepare_cvact.py

    Some questions about the prepare_cvact.py

    Hello, I had some problems while preparing for CVACT, as follows, **dataset index: 218255 dataset unexist pair: ['G:/datasets/ANU_data_small/streetview/HkiKa_k0d5RXDxW14D_A1A_grdView.jpg', 'G:/datasets/ANU_data_small/satview_polish/HkiKa_k0d5RXDxW14D_A1A_satView_polish.jpg']** This kind of prompt appeared many times. After processing, the 70G data finally generated only a 600M data. Can you tell me how to solve this problem?

    opened by Allen-lz 4
Owner
null
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465

PointDSC repository PyTorch implementation of PointDSC for CVPR'2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency",

null 153 Dec 14, 2022
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 3, 2023
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 163 Dec 26, 2022
PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

Asym-Siam: On the Importance of Asymmetry for Siamese Representation Learning This is a PyTorch implementation of the Asym-Siam paper, CVPR 2022: @inp

Meta Research 89 Dec 18, 2022
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

null 967 Jan 4, 2023
This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression Introduction In this paper, we are interested in the bottom-up paradigm of estima

HRNet 367 Dec 27, 2022
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF ?? Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 8, 2023
Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Swin-Transformer-Tensorflow A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Sh

null 52 Dec 29, 2022
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

Predicting Patient Outcomes with Graph Representation Learning This repository contains the code used for Predicting Patient Outcomes with Graph Repre

Emma Rocheteau 76 Dec 22, 2022
https://arxiv.org/abs/2102.11005

LogME LogME: Practical Assessment of Pre-trained Models for Transfer Learning How to use Just feed the features f and labels y to the function, and yo

THUML: Machine Learning Group @ THSS 149 Dec 19, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

A Critical Assessment of State-of-the-Art in Entity Alignment This repository contains the source code for the paper A Critical Assessment of State-of

Max Berrendorf 16 Oct 14, 2022
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 7, 2022
ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

This is the project page for the paper: ISTR: End-to-End Instance Segmentation via Transformers, Jie Hu, Liujuan Cao, Yao Lu, ShengChuan Zhang, Yan Wa

Jie Hu 182 Dec 19, 2022