Model Card for CNN Waste Classification (PyTorch & OpenCV)

A PyTorch Convolutional Neural Network (CNN) for multi-class waste classification using images. Predicts 10 types of waste from static images or real-time webcam streams, supporting applications in smart recycling, education, and research. Uses OpenCV for image handling. Trained on the modified Kaggle Garbage Classification dataset.

Model Details

Model Description

A deep learning model for classifying waste into 10 categories: Battery, Cardboard, Clothes, Food Waste, Glass, Metal, Paper, Plastic, Shoes, and Trash. The model uses 6 convolutional layers with batch normalization, dropout, and two fully connected layers. Developed for learning, prototyping, and proof-of-concept smart recycling systems.

Developed by: Gokul Seetharaman
Model type: Convolutional Neural Network (CNN)
License: MIT
Finetuned from model [optional]: Trained from scratch

Model Sources [optional]

Repository: https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch
Dataset: https://www.kaggle.com/datasets/mostafaabla/garbage-classification

Uses

Direct Use

Image-based waste detection for smart recycling prototypes
Educational demonstrations of CNNs, OpenCV, and PyTorch
Research baselines for waste/material classification

Recommendations

Users should evaluate model performance on their own data and consider retraining or fine-tuning for domain-specific use. It is not recommended to use the model for high-stakes applications without further testing.

How to Get Started with the Model

Download best_model.pth and object-detection.py from this repo or GitHub.
Run python object-detection.py for webcam or image predictions.
Modify object-detection.py to use your own image or video source.

Training Details

Training Data

Kaggle Garbage Classification dataset
10 classes, ~1200 images (split 80/20 train/val)
Preprocessing: resized to 224x224, normalized, data augmentation (crop, flip, rotation, color jitter, affine)

Training Procedure

6 Conv layers, 2 FC layers, dropout, batchnorm
CrossEntropyLoss, AdamW optimizer, 50 epochs, batch size 8

Preprocessing [optional]

Images resized to 224x224
Normalized with ImageNet means/std
Random data augmentation on train set

Training Hyperparameters

Training regime: fp32
Epochs: 50, batch size: 8, optimizer: AdamW, LR: 5e-4

Speeds, Sizes, Times [optional]

Training time: ~90 minutes on a modern GPU (varies)
Checkpoint size: ~46MB (best_model.pth)

Evaluation

Testing Data, Factors & Metrics

Testing Data

20% validation split from the Kaggle dataset (stratified)

Factors

Performance measured per-class (precision, recall, F1-score, support)

Metrics

Overall accuracy, confusion matrix, precision/recall/F1-score per class

Results

Validation accuracy: 89.56%
Most class F1-scores >0.85, with "Plastic" lower due to visual ambiguity
Full confusion matrix and metrics in GitHub README

Summary

The model reliably classifies 10 types of waste in standard settings. See GitHub for sample images and live demo outputs.

Model Examination [optional]

No explicit interpretability/visualization methods (e.g., GradCAM) included yet.

Environmental Impact

Estimated training: <1.5 GPU-hour, carbon footprint minimal for local or single-GPU cloud runs
Hardware: NVIDIA GeForce GTX 4060 Laptop GPU
Hours used: ~1.5

Technical Specifications [optional]

Model Architecture and Objective

See "Model Details" and GitHub repo for the full PyTorch code.

Compute Infrastructure

Local training with NVIDIA GTX 4060 Laptop GPU, 8GB VRAM, 16GB RAM, Windows 11, Python 3.10

Hardware

GPU: GTX 4060 (or equivalent, optional CPU)
RAM: 16GB

Software

Python 3.10, PyTorch, OpenCV, NumPy

Citation

BibTeX:

@misc{gokulseetharaman2025wastecnn,
  title={CNN Waste Classification with OpenCV and PyTorch},
  author={Gokul Seetharaman},
  year={2025},
  url={https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch}
}

APA: Gokul Seetharaman. (2025). CNN Waste Classification with OpenCV and PyTorch. https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch

Model Card Contact

GitHub Issues