AlthariqFairuz/trashnet-classification

Trash Classification CNN

This repository contains a Convolutional Neural Network (CNN) model designed for classifying waste images into six distinct categories.

Model Description

The model implements a deep CNN architecture specifically designed for waste image classification. It processes RGB images through multiple convolutional layers with increasing feature complexity, followed by dense layers for final classification.

Architecture Details

The model uses a progressive feature extraction architecture:

• Input layer for RGB images (3 channels)
• Three convolutional layers with increasing filters (32 → 64 → 128)
• MaxPooling layers after each convolution
• Dropout layers (0.25) for regularization
• Three fully connected layers (128 → 32 → 6)
• ReLU activation functions throughout
• Final layer outputs 6 classes (waste categories)

Dataset and Training

The model was trained on the TrashNet dataset with a careful data splitting strategy:

• Training set: 70% of the data
• Validation set: 20% of the data
• Test set: 10% of the data

The training process utilized comprehensive data augmentation techniques to improve model robustness:

transformers = transforms.Compose([
   transforms.Resize((224, 224)),
   transforms.RandomHorizontalFlip(p=0.5),
   transforms.RandomRotation(degrees=15),
   transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
   transforms.ToTensor(),
   transforms.Normalize(
       mean=[0.485, 0.456, 0.406],
       std=[0.229, 0.224, 0.225]
   )
])