Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+datasets:
+- garythung/trashnet
+---
+
+# 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:
+```python
+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]
+   )
+])