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akmalia31/
trash-classification-cnn-mobilnetv2

Name: trash-classification-cnn-mobilnetv2
Availability: InStock

image-classification

garythung/trashnet

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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

python

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!

akmalia31/trash-classification-cnn-mobilnetv2