Skip to content

Latest commit

 

History

History
99 lines (84 loc) · 5.28 KB

README.md

File metadata and controls

99 lines (84 loc) · 5.28 KB
Raw

Garbage Detection Based on Convolutional Neural Network

Final project for an application of machine learning and deep learning course (ITC C508). The code has been made available as a reference if anyone would like to reproduce the results of this project.

Please cite this project if this repository is useful for your research.

Recycling trash is a crucial part of protecting our environment. Garbage must be divided into categories with similar recycling processes in order to enable the recycling process. The percentage of recycled waste can rise considerably if it is possible to separate domestic trash into several categories. Using the classes given, we trained the model in this notebook to categorize the input images and output the trash classification.

Project Overview

screenshot

  • The objective of this study is to train the model to be able to predict the input images and classify it into one of the garbage recycling categories, which includes cardboard, glass, metal, paper, plastic, trash.
  • The CNN generated an accuracy score of 92.96% which proved that this model has a potential to be used a a tool for predicting garbage clases.
  • Dataset includes cardboard(393), glass(491), metal(400), paper(584), plastic(472), trash(127)

class-distribution

Built with

  • Python
  • Jupyter Notebook

Getting started

To get the local copy of this repository, please run the following commands on your terminal:

git clone https://github.com/PizaaRiaaa/Synapse.git

Libraries

  • tensorflow
  • Sequential
  • keras.layers
  • ImageDataGenerator
  • sklearn.metrics
  • keras
  • PIL
  • pathlib
  • scipy
  • os
  • numpy
  • matplotlib.pyplot
  • torchvisio.datasets
  • torchvision.transform

Data Preprocessing

Steps that were applied for the data preprocessing for all the images in the dataset. Each step is decribed below:

  • Step 1: Resized all the images in the dataset to 32x32 pixel to match the input layer of the model
  • Step 2: Converted all the images to grayscale to simplify the model's algorithm and computational requirements
  • Step 3: Normalized image dataset by dividing each pixel value to 255.The pixel value ranges from 0 to 255 for each the channels (RGB). Dividing it by 255 is necessary to normalize it to 0 to 1 range.
  • Step 4: Flatten all the images into a vector matrix
  • Step 5: Shuffled the train dataset for equal distribution of the data for splitting
  • Step 6: Split the data into three sets - train, test, and validation. This is undergone to avoid data overfitting and to increase the model accuracy score

CNN graph architecture based on the compiled model cnn

Traning the model by using the train dataset

Epoch 1/60 52/52 [==============================] - 17s 318ms/step - loss: 1.7310 - accuracy: 0.2291 - val_loss: 1.7209 - val_accuracy: 0.2467 Epoch 2/60 52/52 [==============================] - 6s 107ms/step - loss: 1.7051 - accuracy: 0.2598 - val_loss: 1.6693 - val_accuracy: 0.2349 Epoch 3/60 52/52 [==============================] - 4s 85ms/step - loss: 1.6228 - accuracy: 0.2892 - val_loss: 1.5717 - val_accuracy: 0.3482 Epoch 4/60 52/52 [==============================] - 5s 96ms/step - loss: 1.5400 - accuracy: 0.3636 - val_loss: 1.5166 - val_accuracy: 0.3449 Epoch 5/60 52/52 [==============================] - 5s 93ms/step - loss: 1.4760 - accuracy: 0.3879 - val_loss: 1.4331 - val_accuracy: 0.4297 Epoch 6/60 52/52 [==============================] - 5s 91ms/step - loss: 1.4269 - accuracy: 0.4342 - val_loss: 1.3399 - val_accuracy: 0.4749 Epoch 7/60 52/52 [==============================] - 5s 92ms/step - loss: 1.3674 - accuracy: 0.4600 - val_loss: 1.3171 - val_accuracy: 0.4760 Epoch 8/60 52/52 [==============================] - 5s 91ms/step - loss: 1.3747 - accuracy: 0.4561 - val_loss: 1.2554 - val_accuracy: 0.5385 Epoch 9/60 52/52 [==============================] - 5s 92ms/step - loss: 1.2953 - accuracy: 0.4943 - val_loss: 1.2450 - val_accuracy: 0.5167 Epoch 10/60 52/52 [==============================] - 5s 90ms/step - loss: 1.2335 - accuracy: 0.5361 - val_loss: 1.1797 - val_accuracy: 0.5469 Epoch 11/60 52/52 [==============================] - 5s 92ms/step - loss: 1.1972 - accuracy: 0.5508 - val_loss: 1.1494 - val_accuracy: 0.5731 Epoch 12/60 52/52 [==============================] - 4s 86ms/step - loss: 1.1498 - accuracy: 0.5658 - val_loss: 1.1558 - val_accuracy: 0.5714 Epoch 13/60 ... Epoch 59/60 52/52 [==============================] - 4s 75ms/step - loss: 0.2195 - accuracy: 0.9278 - val_loss: 0.1807 - val_accuracy: 0.9526 Epoch 60/60 52/52 [==============================] - 4s 76ms/step - loss: 0.2202 - accuracy: 0.9296 - val_loss: 0.2046 - val_accuracy: 0.9308

Results

The image below shows the results by comparing predictions with the original garbage labels. As can be seen, all images were correctly predicted. The image classfied as cardboard has the probability of 99%, glass 87%, metal 99%, paper 96%, plastic 83%, and trash with 98%.

results

License

This project is MIT Licensed