README.md

---
license: mit
language: en
pipeline_tag: image-classification
library_name: pytorch
datasets: mostafaabla/garbage-classification
tags:
- CNN
- Waste-classification
- Image-Classification
---

# Model Card for CNN Waste Classification (PyTorch & OpenCV)

<!-- Provide a quick summary of what the model is/does. -->

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](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch)
* **Dataset:** [https://www.kaggle.com/datasets/mostafaabla/garbage-classification](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

1. Download `best_model.pth` and `object-detection.py` from this repo or [GitHub](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch).
2. Run `python object-detection.py` for webcam or image predictions.
3. Modify `object-detection.py` to use your own image or video source.

## Training Details

### Training Data

* [Kaggle Garbage Classification dataset](https://www.kaggle.com/datasets/mostafaabla/garbage-classification)
* 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](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch#results)

#### 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](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch#model-architecture) 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:**

```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](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch)


## Model Card Contact

[GitHub Issues](https://github.com/gokulseetharaman/cnn-waste-classification-opencv-pytorch/issues)