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--- |
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pipeline_tag: image-classification |
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--- |
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## Location Classification of Indian Cities |
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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%. |
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## How to Use the App |
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1. Clone the GitHub repository: |
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``` |
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git clone https://github.com/shahdivax/Location-Classification-of-Indian-Cities.git --branch master |
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``` |
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2. Install the required libraries: |
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``` |
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pip install -r requirements.txt |
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``` |
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3. Run the app: |
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``` |
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streamlit run app.py |
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``` |
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For Flask app:<br> |
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change Directory |
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``` |
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cd Flask |
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``` |
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run app: |
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``` |
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flask run |
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``` |
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#### Flask Demo: |
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https://github.com/shahdivax/Location-Classification-of-Indian-Cities/assets/61962983/d29652ab-2e07-4b81-bd6d-c4e53c5f3891 |
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<br> |
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4. Upload an image in JPG or JPEG format.<br> |
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5. The app will display the uploaded image and predict the location of the city in the image.<br> |
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6. The predicted location and accuracy percentage will be displayed. |
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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. |
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## Live Demo |
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A live demo of the app is available [here](https://location-classification-of-indian-cities.streamlit.app/) hosted with Streamlit. |
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## Code |
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The code for this app was written in Python. It uses the following libraries: |
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* Streamlit: To build the app user interface |
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* TensorFlow and Keras: To load the pre-trained model and process images |
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* Numpy and Random: For data processing and random color selection |
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The application flow follows the steps below: |
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1. Load the trained deep learning model. |
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2. Define the class labels for the 5 Indian cities. |
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3. Set a minimum accuracy threshold for predictions. |
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4. Create a function to process uploaded images. |
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5. Create a Streamlit app interface with a file uploader. |
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6. Process uploaded images and display the predicted location and accuracy. |
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## Future Work |
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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. |
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