README.md

metadata

title: Trash Classification
colorFrom: green
colorTo: blue
sdk: streamlit
sdk_version: 1.44.1
app_file: src/app.py
pinned: false

Trash Classifier with Explainability

A deep learning project to classify recyclable waste items from images, enhanced with visual explainability tools. This pipeline includes model training, evaluation, and a real-time interactive app to demonstrate classification and model interpretation.

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Purpose and Why This Project Matters

Recycling is a crucial step toward sustainability, but it remains a manual and error-prone process. Automating trash classification helps:

• Improve waste sorting accuracy
• Reduce contamination in recycling streams
• Enable smart waste management systems

This project builds a lightweight yet powerful image classifier that recognizes 6 categories of recyclable and non-recyclable waste, enhanced with saliency-based interpretability to show why the model predicts what it does.

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Architecture Overview

• Backbone Model: MobileNetV2 (pretrained on ImageNet)
• Classifier Head: Fully connected layer with 6 output classes
• Explainability: Captum's Saliency, SmoothGrad, Guided Backpropagation
• Deployment: Streamlit app for real-time prediction and visualization

Dataset Used

• Name: TrashNet
• Classes: cardboard, glass, metal, paper, plastic, trash
• Image Size: 512x384 pixels
• Class Distribution:
  • Paper: 23.51%
  • Glass: 19.83%
  • Plastic: 19.07%
  • Metal: 16.22%
  • Cardboard: 15.95%
  • Trash: 5.42%
• Handling: Dataset split into train, val, test folders with stratification

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Techniques and Tools Used

• Model Training:

  • Transfer learning with frozen base layers
  • Adam optimizer, CrossEntropyLoss
  • TensorBoard for live metric tracking

• Preprocessing:

  • Resize & normalize images to 224x224
  • Stratified train-val-test split

• Explainability:

  • Captum's:
    • Saliency: Base gradients
    • SmoothGrad: Smoothed attribution
    • GuidedBackprop: Edge-focused explanations
  • Visualization: Matplotlib side-by-side comparisons

• Deployment:

  • Streamlit app with file uploader and toggle for saliency types

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Training Procedure

• Epochs: 10
• Optimizer: Adam (lr = 1e-4)
• Loss Function: CrossEntropy
• Early Stopping: Not needed (no overfitting)
• Logging: TensorBoard for accuracy/loss

Training monitored using:

• Accuracy/train and Accuracy/val
• Loss/train and Loss/val

Training Accuracy/Validation Graph:

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Result Evaluation

Metric	Value
Train Accuracy	98.1%
Val Accuracy	91.1%
Train Loss	0.071
Val Loss	0.312

Evaluation Tools:

• TensorBoard visualization
• Saliency-based interpretation (SmoothGrad + Guided)
• Streamlit demo UI

Visual Placeholder:

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Overall Project Impact

• Demonstrates real-world machine learning application
• High performance on imbalanced, noisy data
• Includes interpretability and explainability tools
• Easy-to-use UI for non-technical users

This project shows a complete ML pipeline — from raw image data to deployed app with transparency in decision-making.

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Final Result

A robust trash classifier trained on real data, achieving 91.1% accuracy on validation and equipped with visual explainability, ready to be deployed in sustainability-focused applications, smart bins, or recycling plants.

[Live Demo Link Coming Soon]

[Try Locally]:

streamlit run web_app/app.py

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