https://github.com/aidinhamedi/pytorch-garbage-classification

Welcome to the repository of our garbage classification project! We have developed a model using PyTorch and EfficientNet-B4 that classifies garbage into twelve different types. The model has achieved an impressive accuracy of 98.45%.
https://github.com/aidinhamedi/pytorch-garbage-classification

classification efficientnet garbage-classification garbage-detection pyt python pytorch pytorch-cnn pytorch-tutorial

Last synced: about 1 year ago
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Welcome to the repository of our garbage classification project! We have developed a model using PyTorch and EfficientNet-B4 that classifies garbage into twelve different types. The model has achieved an impressive accuracy of 98.45%.

• Host: GitHub
• URL: https://github.com/aidinhamedi/pytorch-garbage-classification
• Owner: AidinHamedi
• License: mit
• Created: 2024-04-13T14:25:43.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-03T06:28:07.000Z (over 1 year ago)
• Last Synced: 2024-11-26T15:50:20.973Z (over 1 year ago)
• Topics: classification, efficientnet, garbage-classification, garbage-detection, pyt, python, pytorch, pytorch-cnn, pytorch-tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 7.52 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Garbage Classification with PyTorch

    

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Welcome to the repository of our garbage classification project! We have developed a model using PyTorch and EfficientNet-B4 that classifies garbage into twelve different types. The model has achieved an impressive accuracy of **98.45%**.

🚧 I made a new version here: https://github.com/Aydinhamedi/Pytorch-Garbage-Classification-V2 with a significantly improved training process + code and a different dataset 🚧


## Dataset

The dataset used for this project is the [Garbage Classification (12 classes) Dataset](https://www.kaggle.com/datasets/mostafaabla/garbage-classification) from Kaggle. It contains images of garbage, divided into twelve categories.

## Model

We used the EfficientNet-B4 model for this project. EfficientNet-B4 is a convolutional neural network that is pretrained on the ImageNet dataset. It is known for its efficiency and high performance on a variety of image classification tasks.

## Installation

To run the code in this repository, you will need to install the required libraries. You can do this by running the following command:

```bash

pip install -r requirements.txt

```

## Usage

The main code for this project is in a Jupyter notebook named `Main.ipynb`. To run the notebook, use the following command:

```bash

jupyter notebook Main.ipynb

```

## Results

Our model achieved an accuracy of **98.45%** on the test set. This is a significant improvement over previous models, demonstrating the power of EfficientNet-B4 and PyTorch.

## License


 Copyright (c) 2024 Aydin Hamedi

 

 This software is released under the MIT License.

 https://opensource.org/licenses/MIT