Skip to content

GWaste/waste-classifer

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

23 Commits
 
 
 
 
 
 
 
 

Repository files navigation

GWaste: Get to know your Waste

Waste Classifer

This repository contains our notebooks and dataset that we used when exploring our dataset, building our model, and testing our middleware. These notebooks was run on Google Colab environment.

Dataset

We used trashnet by Gary Thung. You can get the original one from here.

We have our two versions of that dataset, which is v1 and v2. Both of this dataset originally from dataset-resized.zip. v1 is the version where we manually classify and merged the "trash" class to the other classes. While v2 is the version with "trash" class entirely removed from the dataset.

Getting Started

Run in Google Colab View source on GitHub Download notebook

For all of process that we do, we are using Google Colab (you can choose on the link above). But you can use local instead by several steps:

Prerequisites

Run Locally

  1. Download notebook

  2. Clone the project [Optional]

    $ git clone https://github.com/GWaste/waste-classifer
  3. Go to the project directory

    $ cd waste-classifer
  4. Install the required library with virtualenv

    • Linux/ MacOS
      $ virtualenv env
      $ source env/bin/activate
      (venv) $ pip install numpy matplotlib tensorflow
    • Windows
      python -m venv env
      env\scripts\activate
      pip install numpy matplotlib tensorflow
  5. Run with jupyter notebook

    $ jupyter notebook

Contributing

Contributions are always welcome!

Feel free to clone, use, and contribute via pull request.

Got an issue? Please use issues panel

We are exciting to see your contributions!

Feedback

If you have any feedback, please reach out to us at b21-cap0331@bangkit.academy or contact one of our member.

Authors

Model

Other member

Related Paper

Here are some related paper that we used:

Fine-Tuning Models Comparisons on Garbage Classification for Recyclability

Comparative Analysis of Multiple Deep CNN Models for Waste Classification

Appendix

Acknowledgements

About

Repository for waste image classification

Resources

Stars

Watchers

Forks

Releases

No releases published