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README.md
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- waste
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- classification
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pretty_name: waste-cl
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- waste
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- classification
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pretty_name: waste-cl
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---
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# Dataset Card for waste classifier
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This dataset contains waste images in different categories:
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- cardboard
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- compost
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- glass
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- metal
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- paper
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- plastic
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- trash
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### Dataset Description
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- **Curated by:** Rootstrap
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- **License:** MIT
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### Dataset Sources
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Data is a combination of [Trashnet](https://github.com/garythung/trashnet) dataset plus more images obtained by internet search.
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Paper: [Classification of Trash for Recyclability Status](https://cs229.stanford.edu/proj2016/report/ThungYang-ClassificationOfTrashForRecyclabilityStatus-report.pdf)
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## Uses
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The dataset can be used for waste classification or other type of project.
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### Direct Use
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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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{{ direct_use | default("[More Information Needed]", true)}}
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## Dataset Structure
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The data is already split in train and test folders.
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Inside each folder contains one folder for each class.
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## Dataset Creation
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### Curation Rationale
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at Rootstrap, our Machine Learning Engineers are committed to creating awareness of correct waste classification to help the environment.
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Their determination to make an impact led to the creation of 'RootTrash', an internal AI-powered app to help us recycle correctly.
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#### Data Collection and Processing
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Some of the images were obtained using Bing searcher using the api HTTP.
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You can find the code used to download the images at this [Google Colab](https://colab.research.google.com/drive/1JvAYFx1DIEi1MMyI-tuCfE2eHMSKisKT?usp=sharing).
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#### Who are the source data producers?
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Thung, G., & Yang, M. (2016). Classification of Trash for Recyclability Status.
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## Bias, Risks, and Limitations
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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.
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In the future, the dataset will be extended with the photos taken through the app.
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### Recommendations
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Integrate this model with a detection model such as [rootstrap-org/waste-detector](https://huggingface.co/rootstrap-org/waste-detector)
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