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fresh-or-rotten

Mlpart

Interactive webapp for classification of images of fruits and vegetables into fresh or rotten made using Convolutional neural networks in python ,tensorflow and keras .Rembg (@rembg,https://github.com/danielgatis/rembg) used for removing the image background .Hosted using streamlit

Link - https://share.streamlit.io/shreyasvaidya/fresh-or-rotten/main/app.py

Working(in case some dependency breaks again)

fresh apple

Code for training saved_model.h5 can be found at https://github.com/Shreyasvaidya/Fresh-rotten_Classifier

Developed as a part of the Engineering Design project website "Annadanam"(@ED-Annadanam)

Team members(Who also worked on other parts of the website)

1.Samarth Sudhirkumar Bhalerao

2.Vikash Kumar

3.Uppala Giridhar

4.Basanti Meena

5.Saurabh kumar Meena

6.Vaidehi Bala(was a part of the team in the first semester,was an integral contributor in ideation)

Credits

Instructors

1.Dr.Manish Narwaria(narwaria@iitj.ac.in)

2.Dr.Sucharita Dey(sdey@iitj.ac.in)

Dataset taken from https://www.kaggle.com/datasets/raghavrpotdar/fresh-and-stale-images-of-fruits-and-vegetables

https://youtube.com/playlist?list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN used For learning how to train a deep learning model

https://www.pluralsight.com/guides/deploying-image-classification-on-the-web-with-streamlit-and-heroku For hosting using streamlit and remotely deploying using heroku

Seniors who provided technical advice

Rohan Singh (https://github.com/rohansingh9001) on where to start and which resources to use

Soham Sonawane (https://github.com/killbotXD) for hosting

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