Common Garbage Classification Using Mobilenet

Common Garbage Classification Using Mobile Net

Abstract:

Garbage classification is the first step in waste segregation, recycling, or reuse. Mobile Net was used to generate a model that classifies common trash according to the following categories: glass, paper, cardboard, plastic, metal, and other trash. A dataset of trash images in .jpg extension was used for the training. The model used transfer learning from a model trained on the ImageNet Large Visual Recognition Challenge dataset. The Tensor Flow for Poets git repository was cloned as a working directory to retrain the Mobile Net model. The Resulting baseline model, with a final test accuracy was optimized and quantized. In the application development, the optimized model is preferred over the test using a plastic image. It is recommended to rerun the training using more steps as this may improve the quantized model performance.