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Plastics image training dataset with annotations for computer vision and deep learning projects

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PlaNet

The PlaNet dataset is being used to detect floating and terra firma waste debris in oceans/ports/harbors/beaches, urban and rural areas allowing the eradication of waste, helping marine life, fishermen, tourism and making the world resilient to climate change by Recyclero.

The dataset has been collected in a joint effort between the Recyclero and the Manipal University Jaipur. Students were able to contribute by sending their pictures of plastics, glass, paper, rubbish, metal and cardboard with our custom-built application.

Dataset

This repository contains the dataset that we collected. The dataset spans six classes: glass, paper, cardboard, plastic, metal, and trash. Currently, the dataset consists of 2527 images,

  • 501 glass
  • 594 paper
  • 403 cardboard
  • 482 plastic
  • 410 metal
  • 137 trash

The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. The pictures have been resized down to 512 x 384, which can be changed in dataset/constants.py (resizing them involves going through step 1 in usage). The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE.

Usage: Preparing the data

If adding more data, then the new files must be enumerated properly and put into the appropriate folder in dataset/original and then preprocessed. Preprocessing the data involves deleting the dataset/resized folder and then calling python resize.py from PlaNet/dataset/*. This will take around half an hour.

Setup

Python is currently used for some image preprocessing tasks. The Python dependencies are,

You can install these packages by running the following,

# Install using pip
pip install numpy scipy

Contributing

  1. Fork the repository
  2. Create your feature branch using git checkout -b my-new-feature
  3. Commit your changes using git commit -m 'Add some feature'
  4. Push to the branch using git push origin my-new-feature
  5. Submit a pull request

Acknowledgments

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Plastics image training dataset with annotations for computer vision and deep learning projects

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