https://github.com/BexTuychiev/pet_pawpularity

Predict the popularit of cats and dogs using deep learning methods
https://github.com/BexTuychiev/pet_pawpularity

Last synced: about 1 month ago
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Predict the popularit of cats and dogs using deep learning methods

• Host: GitHub
• URL: https://github.com/BexTuychiev/pet_pawpularity
• Owner: BexTuychiev
• License: mit
• Created: 2022-02-22T16:22:12.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-06-11T05:53:53.000Z (almost 3 years ago)
• Last Synced: 2024-10-27T19:00:06.204Z (6 months ago)
• Language: Jupyter Notebook
• Size: 18.1 MB
• Stars: 9
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-dogs - Pet Pawpularity

README

        
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/bextuychiev/pet_pawpularity/ui/src/ui.py)








  

    

  


Pet Pawpularity


  


    Predict pet cuteness scores using machine learning.

    


  



  Table of Contents

  


    


      About The Project

      


        
Built With

      

    

    
Detailed information

    
Contact

  


## About The Project

![](images/demo.gif)

Hi, I am Bex! I built this project to create a simple web app that would allow any user to

upload an image of their pet and get a cuteness score. The data comes from Petfinder.my

website (

source) and contains about 10k images with labels for their cuteness. As cuteness is

such a subjective concept, the scores returned from the app are not necessarily accurate.

In fact, even the best solutions to this challenge on Kaggle are very close to a solution

that returns just random scores.

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### Built With

* [DVC](https://dvc.org/)

* [MLFlow](https://mlflow.org/)

* [Streamlit](https://streamlit.io/)

* [BentoML](https://www.bentoml.com/)

* [DagsHub](https://dagshub.com/)

* [Heroku](https://www.heroku.com/)

* [TensorFlow](https://www.tensorflow.org/)

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## Detailed description of the project

I explain my approach to solve the project in three articles

on my Medium blog:

* [Part 1: Project Overview and DVC Setup](https://towardsdatascience.com/open-source-ml-project-with-dagshub-improve-pet-adoption-with-machine-learning-1-e9403f8f7711)

* [Part 2: Detailed tutorial to MLFlow and experiment tracking for the project](https://towardsdatascience.com/complete-guide-to-experiment-tracking-with-mlflow-and-dagshub-a0439479e0b9)

* [Part 3: In-depth Tutorial to deploying the project with the combination of DagsHub, BentoML, Streamlit](https://towardsdatascience.com/the-easiest-way-to-deploy-your-ml-dl-models-in-2022-streamlit-bentoml-dagshub-ccf29c901dac)

You can also try out the API for this project by sending a POST request

to this address. Please, read the

last part of the article for the details.

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## Contact

Bex Tuychiev - [@BexTuychiev](https://www.linkedin.com/in/bextuychiev/) -

[email protected]

Project

Link: [https://github.com/BexTuychiev/pet_pawpularity](https://github.com/BexTuychiev/pet_pawpularity)

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