---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
language:
- en
metrics:
- accuracy
library_name: flair
pipeline_tag: image-classification
tags:
- art
- code
---

# 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:** [https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification]
- **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}')