Model Card: Trash Classification Using MobileNetV2
Model Details
- Model Name: Trash Classification CNN with MobileNetV2
- Model Type: Convolutional Neural Network (CNN)
- Architecture: MobileNetV2
- Dataset: TrashNet Dataset
- Languages: None (Image-based model)
- License: MIT
Model Description
This model classifies images of trash into six categories:
- trash
- plastic
- cardboard
- metal
- paper
- glass
The model is designed to assist in waste segregation and recycling initiatives by automating the identification of waste types. It uses MobileNetV2, a lightweight CNN architecture pre-trained on ImageNet, fine-tuned on the TrashNet dataset for this specific task.
Intended Use
Primary Use Cases
- Waste management systems to automate sorting.
- Educational tools for teaching about recycling and waste segregation.
- Integration into mobile or web applications for real-time waste classification.
Limitations
- Model performance may degrade with images of poor quality or those significantly different from the training dataset.
- Currently supports only six predefined trash categories.
Performance Metrics
- Training Accuracy: 95%
- Testing Accuracy: 90%
- Metrics Evaluated: Accuracy, Precision, Recall, F1-score
- Confusion Matrix: [Available in evaluation results]
How to Use the Model
Input Format
- Images resized to 224x224 pixels and normalized to a range of 0-1.
Output
- A probability distribution over six classes with the predicted label.
Code Example
from transformers import pipeline
from PIL import Image
# Load pre-trained model
classifier = pipeline("image-classification", model="your-model-id")
# Load an image
image = Image.open("sample_image.jpg")
# Perform classification
results = classifier(image)
print(results)
Training Details
- Framework: TensorFlow/Keras
- Optimizer: Adam
- Learning Rate: 0.001
- Loss Function: Categorical Crossentropy
- Batch Size: 32
- Epochs: 20
Data Preprocessing
- Images were resized to 224x224 pixels and normalized.
- Oversampling and data augmentation techniques (rotation, zoom, and rescaling) were applied to handle class imbalance and enhance generalization.
This model card is designed to comply with Hugging Face standards and can be adapted further as needed. Let me know if you need any specific sections expanded!