README.md

---
pipeline_tag: 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. Clone the GitHub repository:
   ```
   git clone https://github.com/shahdivax/Location-Classification-of-Indian-Cities.git --branch master
   ```
2. Install the required libraries:
   ```
   pip install -r requirements.txt
   ```
3. 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/indian_cities_image_classification/resolve/main/Demo.mp4


   <br>
4. Upload an image in JPG or JPEG format.<br>
5. The app will display the uploaded image and predict the location of the city in the image.<br>
6. 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](https://location-classification-of-indian-cities.streamlit.app/) 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. Load the trained deep learning model.
2. Define the class labels for the 5 Indian cities.
3. Set a minimum accuracy threshold for predictions.
4. Create a function to process uploaded images.
5. Create a Streamlit app interface with a file uploader.
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}
}
```