Trash Classifier

Motivation for this Project

Being able to sort trash according to different materials is very important for recycling. However, sorting trash is one of the toughest tasks to do. While it is easy to sort metals and non-metals, it is very difficult to sort paper, glass, plastic, and cardboard.

Currently, it is done by people. It is not a good job and such people are often in danger of being exposed to harmful chemicals, medical wastes and be exposed to diseases. If instead, we can use a neural network that can do the classification then we can make the process faster, safer and more accurate.

This project attempts to use a convolutional neural network to do just that.

Edge Computing

It is not always possible to run a machine learning model on a GPU as there can be cost and space restrictions. And always making an API call can have latencies and internet might not always be available.

In these cases using small, cheap devices at the edge (where the data is generated) is the best solution.

The problem with running models on the edge is that we are limited by the amount of computation power that we have. There are many ways to overcome this. You could use a hardware accelerator like a Neural Computer Stick. Or you could use some models that are built specifically to not be computationally expensive and run on the edge.

In this project, we use the UP Board Embedded Vision Kit to run inference on the edge. We use the MobileNet model which is computationally less expensive.

Results

Project Structure

The whole project is available here

• mobilenet_training.py: You can use this script to train the model.
• trash_classifier.py: The script to classify trash on the UP Board Embedded kit.
• prediction_images: Directory Containing the predicted images.
• models: The saved model

Data

The data for this project was collected from the trashnet project

Training

Sample code for training

python3 mobilenet_training.py --nb_epoch 2 --batch_size 32 --model models/model1.h5

Classification

Sample code for classification.

Note: This will run only with the basler camera.

python3 trash_classifier.py

Requirement

This project requires python3.6 and opencv. Other requirements are present in the requirements.txt file.