dunglucbac / InteriorRecommendation

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Introduction

Have you ever been in a situation where you came to your friend’s house then impressed by his / her apartment decoration. The idea is based on my experience of that and I want a smart machine learning system that can give me similar products on the internet. Moreover, it will give you a better idea by recommending home designs from others to give you a clearer view of what you want in ‘your house’.

Project structure explanation

interior-recommendation
│   README.md             # Project description
│
└───app                   # The folder contains source code
   |               
   └───blueprints
   |   |
   |   └───home
   |        |  
   |        └───blueprints.py  # Receive images from the website then generate recommendation
   |
   └───middlewares
   |    │  ...              
   |               
   └───models           
   |   │  
   |   |─── cropped       # Data class for cropped image
   |   |    ...
   |   └───products       # Deserialize a row in the DataFrame
   |       ...
   └───static             # Bootstrap template
   |    │  ...
   └───templates          # HTML template folder
   |
   main.py                # main function running flask

Instructions for running locally

Clone repository to local machine

git clone git@github.com:dunglucbac/InteriorRecommendation.git

Change directory to local repository

cd InteriorRecommendation

Create python virtual environment

python3 -m venv venv             # create virtualenv
source venv/bin/activate         # activate virtualenv
pip install -r requirements.txt  # install requirements

Run scripts

cd app/
python -m main.py

Check results

image

Link references

https://www.miai.vn/2019/08/09/yolo-series-2-cach-train-yolo-de-detect-cac-object-dac-thu/ https://www.miai.vn/2019/08/15/yolo-series-cach-train-yolo-tren-google-colab/ https://github.com/codingbunnie/WISH-by-NANA-FashionRecommendationEngine https://www.kaggle.com/ekaterinagasparian/train6 https://mc.ai/end-to-end-object-detection-for-furniture-using-deep-learning/

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Language:Python 32.2%Language:HTML 23.2%Language:JavaScript 18.2%Language:CSS 14.5%Language:SCSS 11.9%