https://github.com/omkarpattnaik8080/clothingclassification
"Developing a deep learning model for fashion classification, distinguishing clothing items with high accuracy, leveraging convolutional neural networks for image recognition and feature extraction."
https://github.com/omkarpattnaik8080/clothingclassification
cnn-keras data-science deep-learning machine-learning tensorflow
Last synced: 5 months ago
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"Developing a deep learning model for fashion classification, distinguishing clothing items with high accuracy, leveraging convolutional neural networks for image recognition and feature extraction."
- Host: GitHub
- URL: https://github.com/omkarpattnaik8080/clothingclassification
- Owner: omkarpattnaik8080
- Created: 2024-03-15T17:59:36.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-28T04:57:13.000Z (almost 2 years ago)
- Last Synced: 2025-08-24T13:39:19.467Z (10 months ago)
- Topics: cnn-keras, data-science, deep-learning, machine-learning, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 948 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Clothing Classification with Deep Learning
## Overview
This project focuses on developing a deep learning model for classifying clothing items. Utilizing convolutional neural networks (CNNs), the model accurately identifies various types of apparel from images.
## Requirements
- Python 3.x
- TensorFlow or PyTorch
- NumPy
- Matplotlib
- Fashion MNIST dataset (or similar)
## Usage
1. Prepare the dataset:
- Download the Fashion MNIST dataset or use a similar dataset.
- Organize the dataset into training, validation, and test sets.
2. Train the model:
- Execute the training script.
3. Evaluate the model:
- Run the evaluation script.
4. Predictions:
- Utilize the trained model for predictions on new clothing images.
## Model Architecture
- Convolutional Neural Network (CNN)
- Multiple convolutional layers for feature extraction
- Pooling layers for dimensionality reduction
- Fully connected layers for classification
## Results
- Achieved accuracy: [80.75%]
- Loss curve: [20.25%]
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Inspired by similar projects on clothing classification.
- Grateful for the open-source community for providing valuable resources and datasets.