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rootstrap-org/waste-classifier

Dataset Card for waste classifier This dataset contains waste images in different categories: cardboard compost glass metal paper plastic trash Dataset Sources Data is a combination of Trashnet dataset plus more images obtained by internet search. Paper: Classification of Trash for Recyclability Status Uses The dataset can be used for waste classification or other type of project. Direct Use This dataset is used to build a waste… See the full description on the dataset page: https://huggingface.co/datasets/rootstrap-org/waste-classifier.

sourceHugging Faceupdated 3y agoView on Hugging Face
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Dataset Card

Dataset Card for waste classifier

This dataset contains waste images in different categories:

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

Dataset Description

  • Curated by: Rootstrap
  • License: MIT

Dataset Sources

Data is a combination of Trashnet dataset plus more images obtained by internet search. Paper: Classification of Trash for Recyclability Status

Uses

The dataset can be used for waste classification or other type of project.

Direct Use

This dataset is used to build a waste classifier for categorizing different types of waste, being able to correctly throw the trash in the corresponding trash can at our office.

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Dataset Structure

The data is already split in train and test folders. Inside each folder contains one folder for each class.

Dataset Creation

Curation Rationale

at Rootstrap, our Machine Learning Engineers are committed to creating awareness of correct waste classification to help the environment. Their determination to make an impact led to the creation of 'RootTrash', an internal AI-powered app to help us recycle correctly.

Data Collection and Processing

Some of the images were obtained using Bing searcher using the api HTTP. You can find the code used to download the images at this Google Colab.

Who are the source data producers?

Thung, G., & Yang, M. (2016). Classification of Trash for Recyclability Status.

Bias, Risks, and Limitations

Current model has been trained mostly with internet images and most of them has white background. This might be an issue when testing with real images. In the future, the dataset will be extended with the photos taken through the app.

Recommendations

Integrate this model with a detection model such as rootstrap-org/waste-detector