diabolic6045/indian_cities_image_classification

Location Classification of Indian Cities

This Streamlit app is designed to detect the location of an Indian city in an uploaded image. It uses a deep learning model trained on 10,500 images classified into 5 classes of cities including Ahmedabad, Delhi, Kerala, Kolkata, and Mumbai. The model was trained in association with Parul University and currently has a test accuracy of 66.3%.

How to Use the App

1. 1.Clone the GitHub repository:

   git clone https://github.com/shahdivax/Location-Classification-of-Indian-Cities.git --branch master

1. 1.Install the required libraries:

   pip install -r requirements.txt

1. 1.Run the app:

   streamlit run app.py

For Flask app:<br> change Directory

   cd Flask

run app:

   flask run

#### Flask Demo:

https://huggingface.co/diabolic6045/indiancitiesimage_classification/resolve/main/Demo.mp4

<br>

1. 1.Upload an image in JPG or JPEG format.<br>
2. 2.The app will display the uploaded image and predict the location of the city in the image.<br>
3. 3.The predicted location and accuracy percentage will be displayed.

Please note that the app may not work accurately for images that are not clear or do not have a distinct view of the city's landmarks.

Live Demo

A live demo of the app is available here hosted with Streamlit.

Code

The code for this app was written in Python. It uses the following libraries:

• —Streamlit: To build the app user interface
• —TensorFlow and Keras: To load the pre-trained model and process images
• —Numpy and Random: For data processing and random color selection

The application flow follows the steps below:

1. 1.Load the trained deep learning model.
2. 2.Define the class labels for the 5 Indian cities.
3. 3.Set a minimum accuracy threshold for predictions.
4. 4.Create a function to process uploaded images.
5. 5.Create a Streamlit app interface with a file uploader.
6. 6.Process uploaded images and display the predicted location and accuracy.

Future Work

This app can be improved by increasing the size of the training dataset and fine-tuning the pre-trained model to increase its accuracy. Additionally, the app can be trained to recognize city landmarks to improve its performance.

arXiv : https://arxiv.org/abs/2403.10912

@article{karangiya2024automatic,
  title={Automatic location detection based on deep learning},
  author={Karangiya, Anjali and Sharma, Anirudh and Shah, Divax and Badgujar, Kartavya and Thacker, Dr Chintan and Dave, Dainik},
  journal={arXiv preprint arXiv:2403.10912},
  year={2024}
}