https://github.com/aidinhamedi/pytorch-garbage-classification-v2

A Pytorch project for garbage classification using the EfficientNet-B6 model to achive a 95.78% accuracy on the test set. 😊

Science Score: 23.0%

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Keywords

ai artificial-intelligence classification computer-science computer-vision convolutional-neural-networks deep-learning example-project garbage-classification machine-learning python python3 pytorch template

Last synced: 6 months ago · JSON representation

Repository

A Pytorch project for garbage classification using the EfficientNet-B6 model to achive a 95.78% accuracy on the test set. 😊

Basic Info

Host: GitHub
Owner: AidinHamedi
License: mit
Language: Jupyter Notebook
Default Branch: master
Homepage:
Size: 62.7 MB

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Stars: 1
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Open Issues: 0
Releases: 1

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Created over 1 year ago · Last pushed over 1 year ago

Metadata Files

Readme License

README.md

Garbage Classification V2 with PyTorch

A Pytorch project for garbage classification using the EfficientNet-B6 model to achieve a 95.78% accuracy on the test set.

[!IMPORTANT] This project is a new version of the original project, which can be found here but with a significantly improved training process + code and a different dataset.

😉 Bonus

This project is not hard coded for this specific dataset, so it can be used for any image classification task and it has all the necessary tools to train a model from scratch and more. (I will make a pytorch classification template soon)

📦 Release

Newest release 📃

Go to newest release

📂 Dataset

The dataset used for this project is the Garbage Classification from Kaggle. It contains images of garbage, divided into six categories.

Data Structure

~~~ ├───Database │ └───Data # Put all the folders with images here

Example ⬎

│ ├───battery │ ├───biological │ ├───brown-glass │ ... │ └───white-glass ~~~

🧪 Model

I used the EfficientNet-B6 model for this project. EfficientNet-B6 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. Original paper

🔰 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

[!WARNING] The requirements are auto generated by pipreqs and may not contain all the necessary dependencies. like hidden ones like Tensorboard.

🚀 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

| Metric | Value | |----------------------------------|-----------| | Loss | 0.0330466 | | F1 Score (macro) | 0.95472 | | Precision (macro) | 0.952111 | | Recall (macro) | 0.957959 | | AUROC | 0.993324 | | Accuracy | 0.957839 | | Cohen's Kappa | 0.948292 | | Matthews Correlation Coefficient | 0.948374 |

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📚 License

 Copyright (c) 2024 Aydin Hamedi
 
 This software is released under the MIT License.
 https://opensource.org/licenses/MIT

Owner

Name: Aidin
Login: AidinHamedi
Kind: user

Repositories: 1
Profile: https://github.com/AidinHamedi

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Dependencies

requirements.txt pypi

Pillow *
colorama *
efficientnet_pytorch *
matplotlib *
numba *
numpy *
opencv_python *
pytorch_optimizer *
scikit_learn *
seaborn *
tabulate *
timm *
torch *
torchcam *
torchinfo *
torchvision *
tqdm *