CSL2050 CourseProject

TOPIC : Image Retrieval

Team Members:

• Akshat Jain (B22CS096)
• Harshiv Shah (B22CS098)
• Mehta Jay Kamalkumar (B22CS034)
• Jyotin Goel (B22AI063)
• Rhythm Baghel (B22CS042)

Demo

RiyalNet

QuickNet

Repository Structure

├── Experiments
│   ├── ann_cifar.py
│   ├── centroid_res18.py
│   ├── cifar_10_knn.ipynb
│   ├── cifar_10_knn_resnet18_73_percent.ipynb
│   ├── cifar_10_pca_knn.ipynb
│   ├── cifar_10_res50_m.py
│   ├── cnn-resnet34-cifar10.ipynb
│   ├── Embedding_similarity.ipynb
│   ├── HOG+KNN.ipynb
│   ├── __init__.py
│   ├── Logs
│   │   ├── RESNET50_3HLL_CIFAR.out
│   │   └── RESNET50_CIFAR.out
│   ├── PCA+HOG+KNN.ipynb
│   ├── quicknet_cifar10_centroid.ipynb
│   ├── quicknet_knn.py
│   ├── Resnet50_classification.py
│   ├── train_resnet50_3hll.py
│   └── train_resnet50_iter_1.py
├── flagged
│   └── log.csv
├── images
│   ├── akshat.jpeg
│   ├── dog.jpg
│   ├── harshiv.jpg
│   ├── horse.jpg
│   ├── image2image.png
│   ├── jay.jpeg
│   ├── jyotin.jpeg
│   ├── plane.jpg
│   ├── rhythm.jpeg
│   └── truck.jpg
├── index.html
├── __init__.py
├── LICENSE
├── MidTerm_Report.pdf
├── Model
│   ├── ann.pt
│   ├── centroid_app.py
│   ├── CIFAR.pt
│   ├── data
│   │   ├── batches.meta
│   │   ├── data_batch_1
│   │   ├── data_batch_2
│   │   ├── data_batch_3
│   │   ├── data_batch_4
│   │   ├── data_batch_5
│   │   ├── mean_embeddings.pkl
│   │   ├── test_batch
│   │   └── train_embeddings_resnet18.pkl
│   ├── pretrained_model_weights.h5
│   ├── pretrained_weights_quicknet.py
│   ├── resnet18.h5
│   ├── Resnet50_train_features.pt
│   └── test.py
├── Preprocessing
│   ├── cifar_eda.ipynb
│   ├── k_means.py
│   └── utils.py
├── __pycache__
├── quicknet.gif
├── README.md
├── requirements.txt
├── riyalnet.gif
├── styles
│   └── style.css
└── ui_gradio.py

Image Retriever Installation Guide

This guide will help you set up and install the necessary dependencies for running the project.

Installation Steps

1. Clone the Repository:

   git clone https://github.com/gjyotin305/CSL2050_CourseProject.git

2. cd CSL2050_CourseProject

3. pip install -r requirements.txt

4. python ui_gradio.py
   

This command will start the Gradio interface and display the URL where you can access it. By default, it will run on http://127.0.0.1:7860/.

If you want to specify a custom IP address, you can change the argument of demo.launch() by inserting server_name = "YOUR_IP_ADDRESS". Alternatively, you can use share=True to generate a public link.