Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Overview

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchmarks. New annotation for both datasets is created with an extra attention to the reliability of the ground truth and three new protocols of varying difficulty are introduced. We additionally introduce 15 new challenging queries per dataset and a new set of 1M hard distractors.

This package provides support in downloading and using the new benchmark.

MATLAB

Tested with MATLAB R2017a on Debian 8.1.

Process images

This example script first downloads dataset images and the revisited annotation files. Then, it describes how to: read and process database images; read, crop and process query images:

>> example_process_images

Similarly, this example script first downloads one million images from the revisited distractor dataset (this can take a while). Then, it describes how to read and process images.

>> example_process_distractors

Evaluate results

Example script that describes how to evaluate according to the revisited annotation and the three protocol setups:

>> example_evaluate

It automatically downloads dataset images, the revisited annotation file, and example features (R-[37]-GeM from the paper) to be used in the evaluation. The final output should look like this (depending on the selected test_dataset):

>> roxford5k: mAP E: 84.81, M: 64.67, H: 38.47
>> roxford5k: [email protected][1 5 10] E: [97.06 92.06 86.49], M: [97.14 90.67 84.67], H: [81.43 63.00 53.00]

or

>> rparis6k: mAP E: 92.12, M: 77.20, H: 56.32
>> rparis6k: [email protected][1 5 10] E: [100.00 97.14 96.14], M: [100.00 98.86 98.14], H: [94.29 90.29 89.14]

Python

Tested with Python 3.5.3 on Debian 8.1.

Process images

This example script first downloads dataset images and the revisited annotation files. Then, it describes how to: read and process database images; read, crop and process query images:

>> python3 example_process_images

Similarly, this example script first downloads one million images from the revisited distractor dataset (this can take a while). Then, it describes how to read and process images.

>> python3 example_process_distractors

Evaluate results

Example script that describes how to evaluate according to the revisited annotation and the three protocol setups:

>> python3 example_evaluate

It automatically downloads dataset images, revisited annotation file, and example features (R-[37]-GeM from the paper) to be used in the evaluation. The final output should look like this (depending on the selected test_dataset):

>> roxford5k: mAP E: 84.81, M: 64.67, H: 38.47
>> roxford5k: [email protected][ 1  5 10] E: [97.06 92.06 86.49], M: [97.14 90.67 84.67], H: [81.43 63.   53.  ]

or

>> rparis6k: mAP E: 92.12, M: 77.2, H: 56.32
>> rparis6k: [email protected][ 1  5 10] E: [100.    97.14  96.14], M: [100.    98.86  98.14], H: [94.29 90.29 89.14]

Related publication

@inproceedings{RITAC18,
 author = {Radenovi\'{c}, F. and Iscen, A. and Tolias, G. and Avrithis, Y. and Chum, O.},
 title = {Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
 booktitle = {CVPR},
 year = {2018}
}
Owner
Filip Radenovic
Research Scientist at Facebook
Filip Radenovic
This is the code for the paper "Contrastive Clustering" (AAAI 2021)

Contrastive Clustering (CC) This is the code for the paper "Contrastive Clustering" (AAAI 2021) Dependency python=3.7 pytorch=1.6.0 torchvision=0.8

Yunfan Li 210 Dec 30, 2022
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023
Project page for End-to-end Recovery of Human Shape and Pose

End-to-end Recovery of Human Shape and Pose Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018 Project Page Requirements Pyt

1.4k Dec 29, 2022
Advanced Signal Processing Notebooks and Tutorials

Advanced Digital Signal Processing Notebooks and Tutorials Prof. Dr. -Ing. Gerald Schuller Jupyter Notebooks and Videos: Renato Profeta Applied Media

Guitars.AI 115 Dec 13, 2022
Existing Literature about Machine Unlearning

Machine Unlearning Papers 2021 Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021. Bourtoule et al. Machine Unlearning. In IEEE Symp

Jonathan Brophy 213 Jan 08, 2023
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN)

Flickr-Faces-HQ Dataset (FFHQ) Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative

NVIDIA Research Projects 2.9k Dec 28, 2022
[NeurIPS 2020] Official repository for the project "Listening to Sound of Silence for Speech Denoising"

Listening to Sounds of Silence for Speech Denoising Introduction This is the repository of the "Listening to Sounds of Silence for Speech Denoising" p

Henry Xu 40 Dec 20, 2022
OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

English | 简体中文 Documentation: https://mmtracking.readthedocs.io/ Introduction MMTracking is an open source video perception toolbox based on PyTorch.

OpenMMLab 2.7k Jan 08, 2023
Rotation Robust Descriptors

RoRD Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching Project Page | Paper link Evaluation and Datasets MMA : Training on

Udit Singh Parihar 25 Nov 15, 2022
Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Low-light Image Enhancement via Breaking Down the Darkness by Qiming Hu, Xiaojie Guo. 1. Dependencies Python3 PyTorch=1.0 OpenCV-Python, TensorboardX

Qiming Hu 30 Jan 01, 2023
[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning [MedIA or Arxiv] and [Demo] This repository pr

Healthcare Intelligence Laboratory 92 Dec 08, 2022
Face Alignment using python

Face Alignment Face Alignment using python Input Image Aligned Face Aligned Face Aligned Face Input Image Aligned Face Input Image Aligned Face Instal

Sajjad Aemmi 28 Nov 23, 2022
Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Simple software to calculate susequence with highest sum This repository contain

Gbadamosi Farouk 1 Jan 31, 2022
Knowledge Distillation Toolbox for Semantic Segmentation

SegDistill: Toolbox for Knowledge Distillation on Semantic Segmentation Networks This repo contains the supported code and configuration files for Seg

9 Dec 12, 2022
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer A novel graph neural network (GNN) based model (termed SlideGraph+

28 Dec 24, 2022
PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

48 Dec 08, 2022
MoCap-Solver: A Neural Solver for Optical Motion Capture Data

MoCap-Solver is a data-driven-based robust marker denoising method, which takes raw mocap markers as input and outputs corresponding clean markers and skeleton motions.

55 Dec 28, 2022
Extreme Lightwegith Portrait Segmentation

Extreme Lightwegith Portrait Segmentation Please go to this link to download code Requirements python 3 pytorch = 0.4.1 torchvision==0.2.1 opencv-pyt

HYOJINPARK 59 Dec 16, 2022
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Agustinus Kristiadi 7k Jan 02, 2023
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip

15 Dec 04, 2022