https://github.com/sdsubhajitdas/multi-label-image-classification-demo

B.Tech Final year project demo
https://github.com/sdsubhajitdas/multi-label-image-classification-demo

deep-learning demonstration flask-application image-classification image-recognition mobilenetv2 tensorflow transfer-learning

Last synced: about 1 year ago
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B.Tech Final year project demo

• Host: GitHub
• URL: https://github.com/sdsubhajitdas/multi-label-image-classification-demo
• Owner: sdsubhajitdas
• License: mit
• Created: 2020-04-06T14:48:15.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T10:16:37.000Z (over 3 years ago)
• Last Synced: 2025-03-27T05:23:40.018Z (about 1 year ago)
• Topics: deep-learning, demonstration, flask-application, image-classification, image-recognition, mobilenetv2, tensorflow, transfer-learning
• Language: Python
• Homepage:
• Size: 15.8 MB
• Stars: 5
• Watchers: 1
• Forks: 1
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Multi Label Image Classification Demo

A simple and functional demo website for my B.Tech final year project demo.

## Getting started.

You need to have `python3` and `pip3` in your system.

### Installing

- First clone the project

  ```

  git clone https://github.com/sdsubhajitdas/Multi-Label-Image-Classification-Demo.git

  ```

- (Optional) If you like you can use virtualenv to create a private environment and then activate it.

- Installing the required packages.

  ```

  pip install -r requirements.txt

  ```

### Usage

- Running the project is very simple. It is a simple flask application which is serving a tensorflow model already pretrained & included in the repository. 


  Run this command to start the local server.

  ```

  python run.py

  ```

  This should give a output like this.


  ![Terminal Output](screenshots/1.jpg)

- Copy the url shown in the terminal and open in your browser. You will be greeted with a page like this.

  ![Homepage](screenshots/2.jpg)

## Outputs

Our demo can identify 20 different classes of objects. Below are the following classes.

```

Aeroplane, Bicycle, Bird, Boat, Bottle, Bus, Car, Cat, Chair, Cow, Dining Table,

Dog, Horse, Motorbike, Person, Potted Plant, Sheep, Sofa, Train, Tvmonitor

```

If you are interested about all the training, model generation work, what methods and architeture we have used then you will have to wait. Will publish the other repository soon [here](#) ***(No link yet)***.

Some of the results are below. Click to see the outputs.


Output 1



![Output 1](screenshots/3.jpg)



Output 2



![Output 2](screenshots/4.jpg)



Output 3



![Output 3](screenshots/5.jpg)



Output 4



![Output 4](screenshots/6.jpg)



Output 5



![Output 5](screenshots/7.jpg)



Output 6



![Output 6](screenshots/8.jpg)



Output 7



![Output 7](screenshots/9.jpg)



## Contributors

- [Adrika Nandi](https://www.linkedin.com/in/adrika-nandi-91961015b/)

- [Angana Bose](https://www.linkedin.com/in/angana-bose-4b0a10143/)

- [Debolina Lahiri](https://www.facebook.com/debolina.lahiri.73)

- [Ishani Banerjee](https://www.facebook.com/rimi.banerjee.54390)

- [Subhajit Das](https://sdsubhajitdas.github.io/)

- [Urmila Saha](https://www.facebook.com/urmila.saha.790)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details