"Good artists create, great artists steal"

Code Credits: Universidad de la República, Uruguay URL: https://www.rootstrap.com/blog/how-to-build-a-waste-classifier-app-using-ai Repo: https://github.com/xyfer17/Waste-classification-app

Fastai for Zerowaste365 is a deep learning library which provides high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains.

It has two main design goals:

To be approachable and rapidly productive To be also configurable.

The aim is to build a model for waste classification that identifies among the different classes:

• cardboard
• compost
• glass
• metal
• paper
• plastic
• trash

This machine learning model will help people to improve their decision when classifying trash

The data is already splitted in train and test folders. Inside each folder contains one folder for each class. Those images were obtained using Bing searcher using the api HTTP. You can find the code used to download the images at this Google Colab. You can find the final dataset splitted into train and test here in a Google Drive folder.

Split into train/test

You can find the code for the classifier resnet-model.ipynb To load the model in the file result-resnet34.pkl

The model reaches an accuracy of 0.98, here we can see the confusion matrix:

docker buildx build --platform linux/amd64 -t waste-classifier .

The model is uploaded at this url http://waste-classifer.rs-sandbox.net/

Check this Google Colab to use the model with your own images.