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sriram7737/
TRASHPRED

Name: TRASHPRED
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TRASHPRED: Waste Classification Model

Model Overview

TRASHPRED is a convolutional neural network (CNN) model designed to classify images of waste into categories such as plastic, metal, paper, glass, and organic materials. The model aims to assist in automated waste segregation systems by accurately identifying waste types from images.

Training Details

Framework: TensorFlow with Keras API
Architecture: Custom CNN with multiple convolutional and pooling layers
Dataset: Trained on a curated dataset of labeled waste images
Input Size: 224x224 RGB images
Optimization: Adam optimizer with categorical cross-entropy loss
Epochs: 25
Batch Size: 32

Performance Metrics

Training Accuracy: 95%
Validation Accuracy: 92%
Loss: Monitored using validation loss to prevent overfitting

Usage

To use the TRASHPRED model for inference:

python

import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the model
model = tf.keras.models.load_model('path_to_trashpred_model.h5')

# Load and preprocess the image
img = image.load_img('path_to_image.jpg', target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0

# Predict
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)

print(f"Predicted class: {predicted_class}")
Repository Structure

TRASHPRED/
├── model/
│   └── trashpred_model.h5
├── datasets/
│   ├── train/
│   └── validation/
├── scripts/
│   ├── train.py
│   └── evaluate.py
├── README.md
📄 License
This project is licensed under the MIT License.

Author
Name: Sriram Rampelli

For more projects and information, visit Sriram Rampelli's GitHub Profile.

sriram7737/TRASHPRED