This repository contains a CBIR system that uses swin transformer to extract image's feature.

Overview

Swin-transformer based CBIR

This repository contains a CBIR(content-based image retrieval) system. Here we use Swin-transformer to extract query image's feature, and retrieve similar ones from image database. Notably, our program achieves intelligent user interaction, including selecting an image by opening explorer dialog and cropping interested region by drafting mouse.

framework

Structure

SWIN_CBIR/
|-- checkpoints/
|
|-- database/
|   |-- data/
|   |   |-- 1.jpg
|   |   |-- 2.jpg
|   |  
|   |-- DB.npz
|   |-- index.txt
|
|-- models/
|   |-- __init__.py
|   |-- build.py
|   |-- swin_transformer.py
|
|-- scripts/
|   |-- generate_DB.sh
|
|-- test/
|
|-- config.py
|-- database.py
|-- generate_DB.py
|-- main.py
|-- requirements.txt
|-- README

Getting Started

  1. Prepare images database

    Just find out some images and put them into database/data/.

  2. run ./script/generate_DB.sh in linux machine to extract features of all images and package them into DB.npz.

  3. run main.py, open an image and select interested region, then program will find similar images in database automatically!

Results

Here we show two image retrieval results. Two images in the first row are original image and cropped image respectively while the others are retrieval results (have been sorted by similarity).

Note: all images are resize to square for visual requirement, so there would be distorted in some of the images.

frameworkframework

Acknowledgments

Part of code in this repository are copied from Swin-transformer, thank the authors for their exquiste code.

Owner
JsHou
JsHou
This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."

Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers This repository contains code to run experiments in the paper "Signal Stre

0 Jan 19, 2022
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
COD-Rank-Localize-and-Segment (CVPR2021)

COD-Rank-Localize-and-Segment (CVPR2021) Simultaneously Localize, Segment and Rank the Camouflaged Objects Full camouflage fixation training dataset i

JingZhang 52 Dec 20, 2022
URIE: Universal Image Enhancementfor Visual Recognition in the Wild

URIE: Universal Image Enhancementfor Visual Recognition in the Wild This is the implementation of the paper "URIE: Universal Image Enhancement for Vis

Taeyoung Son 43 Sep 12, 2022
Python-kafka-reset-consumergroup-offset-example - Python Kafka reset consumergroup offset example

Python Kafka reset consumergroup offset example This is a simple example of how

Willi Carlsen 1 Feb 16, 2022
Implementation for "Seamless Manga Inpainting with Semantics Awareness" (SIGGRAPH 2021 issue)

Seamless Manga Inpainting with Semantics Awareness [SIGGRAPH 2021](To appear) | Project Website | BibTex Introduction: Manga inpainting fills up the d

101 Jan 01, 2023
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper) (Accepted for oral presentation at ACM

Minha Kim 1 Nov 12, 2021
Sample code from the Neural Networks from Scratch book.

Neural Networks from Scratch (NNFS) book code Code from the NNFS book (https://nnfs.io) separated by chapter.

Harrison 172 Dec 31, 2022
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl

idealo 4k Jan 08, 2023
Unrolled Generative Adversarial Networks

Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein arxiv:1611.02163 This repo contains an example notebo

Ben Poole 292 Dec 06, 2022
The official pytorch implemention of the CVPR paper "Temporal Modulation Network for Controllable Space-Time Video Super-Resolution".

This is the official PyTorch implementation of TMNet in the CVPR 2021 paper "Temporal Modulation Network for Controllable Space-Time VideoSuper-Resolu

Gang Xu 95 Oct 24, 2022
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Rajaswa Patil 108 Dec 12, 2022
Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch

Semantic Segmentation Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch Features Applicable to followin

sithu3 530 Jan 05, 2023
project page for VinVL

VinVL: Revisiting Visual Representations in Vision-Language Models Updates 02/28/2021: Project page built. Introduction This repository is the project

308 Jan 09, 2023
A deep learning framework for historical document image analysis

DIVA-DAF Description A deep learning framework for historical document image analysis. How to run Install dependencies # clone project git clone https

9 Aug 04, 2022
A more easy-to-use implementation of KPConv

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 35 Dec 14, 2022
Supporting code for the Neograd algorithm

Neograd This repo supports the paper Neograd: Gradient Descent with a Near-Ideal Learning Rate, which introduces the algorithm "Neograd". The paper an

Michael Zimmer 12 May 01, 2022
A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.

Continuous Wasserstein-2 Benchmark This is the official Python implementation of the NeurIPS 2021 paper Do Neural Optimal Transport Solvers Work? A Co

Alexander 22 Dec 12, 2022
Public implementation of "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression" from CoRL'21

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

19 Dec 12, 2022
The implementation of PEMP in paper "Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes"

Prior-Enhanced network with Meta-Prototypes (PEMP) This is the PyTorch implementation of PEMP. Overview of PEMP Meta-Prototypes & Adaptive Prototypes

Jianwei ZHANG 8 Oct 14, 2021