https://github.com/sayannath/identical-image-retrieval

Identical-Image-Retrieval using Deep Learning
https://github.com/sayannath/identical-image-retrieval

deep-learning image-retrieval keras tensorflow

Last synced: 3 months ago
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Identical-Image-Retrieval using Deep Learning

• Host: GitHub
• URL: https://github.com/sayannath/identical-image-retrieval
• Owner: sayannath
• License: apache-2.0
• Created: 2021-08-01T19:32:37.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-08-16T17:06:11.000Z (over 3 years ago)
• Last Synced: 2024-11-02T12:11:48.581Z (3 months ago)
• Topics: deep-learning, image-retrieval, keras, tensorflow
• Language: Jupyter Notebook
• Homepage: https://ijirt.org/Article?manuscript=155052
• Size: 20.6 MB
• Stars: 11
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Identical-Image-Retrieval

## Abstract

In recent years, we know that the interaction with images has increased. Image similarity involves fetching similar-looking images abiding by a given reference image. The target is to find out whether the image searched as a query can result in similar pictures. We are using the BigTransfer Model, which is a state-of-art model itself. BigTransfer(BiT) is essentially a ResNet but pre-trained on a larger dataset like ImageNet and ImageNet-21k with additional modifications. Using the fine-tuned pre-trained Convolution Neural Network Model, we extract the key features and train on the K- Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time. We analyse the benchmark of our model based on this application.

## Description

This project presents a simple framework to retrieve images similar to a query image using Deep Learning. The framework is as follows:

* Train a CNN model (A) on a set of labeled images with Triplet Loss (I used this one).

* Use the trained CNN model (A) to extract features from the validation set.

* Train a kNN model (B) on these extracted features with k set to the number of neighbors wanted.

* Grab an image (I) from the validation set and extract its features using the same CNN model (A).

* Use the same kNN model (B) to calculate the nearest neighbors of I.



I experimented with the Flower Dataset.



## Model Used

I fine-tuned pre-trained models for minimizing the Triplet Loss. I experimented with the following pre-trained models:

* BigTransfer Model (also referred to as BiT) which is essentially a ResNet but pre-trained on a larger dataset with additional modifications.

### Train Graph



### Visualization of the embedding space





## Results