Content-Based Image Retrieval

Introduction

This is my project built for the Content-based Image Retrieval problem. In this project, I use the algorithm of indexing and searching Faiss (Facebook). Simultaneously combine many feature extraction methods for comparison and evaluation (RGBHistogram, Local Binary Pattern, VGG16, ResNet50).

Problem

Input: A collection of information resources (image database), a query image
Output: A ranked list of images that are most similar to the query image, with the most similar image at the top of the list

I use the faiss library created by Facebook. The weights of the VGG16 and Resnet50 networks are taken from the pre-trained model of torchvision.models.

Prepare the environment

python==3.8.16

Install PyTorch-cuda==11.7 following official instruction:

 conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Install Facebook faiss:

 conda install -c conda-forge faiss-gpu

Install the necessary dependencies by running:
```
 pip install -r requirements.txt
```

Prepare the dataset

Put the downloaded The Paris Dataset (or link Kaggle) in ./data/paris
Put the downloaded groundtruth in ./data/groundtruth

Please organize your dataset following this structure:

Main-folder/
│
├── dataset/ 
│   ├── evaluation
|   |   ├── crop
|   |   |   ├── LBP
|   |   |   |   ├── defense_1.txt
|   |   |   |   ├── eiffel_1.txt
|   |   |   |   └── ...
|   |   |   ├── Resnet50
|   |   |   |   └── ...
|   |   |   ├── RGBHistogram
|   |   |   |   └── ...
|   |   |   └── VGG16
|   |   |       └── ...
|   |   └── original
|   |       └── ...
|   |
│   ├── feature
|   |   ├── LBP.index.bin
|   |   ├── Resnet50.index.bin
|   |   ├── RGBHistogram.index.bin
|   |   └── VGG16.index.bin
|   |   
|   ├── groundtruth
|   |   ├── defense_1_good.txt
|   |   ├── eiffel_4_query.txt
|   |   └── ...
|   |
|   └── paris
|       ├── paris_defense_000000.jpg
|       ├── paris_defense_000042.jpg
|       └── ...
|   
└── ...

Running the code

Feature extraction (Indexing)

python indexing.py --feature_extractor Resnet50

The Resnet50.index.bin file will be at Main-folder/dataset/feature.

Evaluation

Evaluation on query set

python ranking.py --feature_extractor Resnet50

Compute Mean Average Precision (MAP):

python evaluate.py --feature_extractor Resnet50

Run demo with streamlit interface:

streamlit run demo.py

Addition

You can modify the config like feature_extractor (RGBHistogram, LBP, VGG16, Resnet50), batch_size, top_k, ...

KhaLee2307/image-retrieval