This repository contains a Convolutional Neural Network (CNN) model designed for classifying waste images into six distinct categories.
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.
The model was trained on the TrashNet dataset with a careful data splitting strategy:
The training process utilized comprehensive data augmentation techniques to improve model robustness:
1transformers = transforms.Compose([
2 transforms.Resize((224, 224)),
3 transforms.RandomHorizontalFlip(p=0.5),
4 transforms.RandomRotation(degrees=15),
5 transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
6 transforms.ToTensor(),
7 transforms.Normalize(
8 mean=[0.485, 0.456, 0.406],
9 std=[0.229, 0.224, 0.225]
10 )
11])
