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README.md

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-README.MD
+---
+license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb-edu
+language:
+- en
+- is
+metrics:
+- accuracy
+library_name: flair
+pipeline_tag: image-classification
+tags:
+- not-for-all-audiences
+---
+
+# Model Card for Custom CNN Model for Garbage Classification
+
+This model card provides information about a custom Convolutional Neural Network (CNN) designed for classifying images of garbage items into predefined categories.
+
+## Model Details
+
+### Model Description
+
+The CNN architecture (CNNModel) consists of:
+
+- Four convolutional layers with batch normalization, ReLU activation, max pooling, and dropout for feature extraction.
+- Two fully connected layers for classification.
+
+The model is trained using the Adam optimizer with cross-entropy loss and a ReduceLROnPlateau scheduler.
+
+### Model Source
+
+- **Repository:** [Link to repository]
+- **Demo:** [Link to demo]
+- **License:** Apache 2.0
+
+## Uses
+
+### Direct Use
+
+This model can be used to classify images of garbage items into specific categories.
+
+### Downstream Use
+
+Fine-tuning the model on a specific garbage classification dataset or integrating it into an application for waste management.
+
+### Out-of-Scope Use
+
+This model is not suitable for general image classification tasks outside of garbage classification.
+
+## Bias, Risks, and Limitations
+
+The model's performance may be affected by biases in the training data, such as underrepresentation of certain garbage types.
+
+### Recommendations
+
+Users should be aware of the model's limitations and consider domain-specific data augmentation to improve performance.
+
+## How to Get Started with the Model
+
+### Example Usage
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Define your CNN model
+class CNNModel(nn.Module):
+    def __init__(self, num_classes):
+        # Define layers as per your CNNModel definition
+
+    def forward(self, x):
+        # Define forward pass as per your CNNModel forward method
+
+# Set device to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the model
+model = CNNModel(num_classes=num_classes).to(device)
+
+# Load the best trained weights
+model.load_state_dict(torch.load('best_model.pth'))
+model.eval()
+
+# Preprocess an image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+img_path = 'path_to_your_image.jpg'  # Replace with your image path
+img = Image.open(img_path)
+input_tensor = transform(img)
+input_batch = input_tensor.unsqueeze(0)
+
+# Use the model for prediction
+with torch.no_grad():
+    output = model(input_batch)
+
+# Get the predicted class
+_, predicted = torch.max(output, 1)
+predicted_class = train_dataset.classes[predicted.item()]
+print(f'Predicted class: {predicted_class}')