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https://github.com/priyapuranik/shoe-classification_using-cnn
A deep learning project that classifies different types of shoes using Convolutional Neural Networks (CNNs) based on image data.
https://github.com/priyapuranik/shoe-classification_using-cnn
cv2 deep-learning keras matplotlib-pyplot python tensorflow
Last synced: 25 days ago
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A deep learning project that classifies different types of shoes using Convolutional Neural Networks (CNNs) based on image data.
- Host: GitHub
- URL: https://github.com/priyapuranik/shoe-classification_using-cnn
- Owner: priyapuranik
- Created: 2024-08-31T12:12:31.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T05:25:06.000Z (about 2 months ago)
- Last Synced: 2024-11-12T06:24:01.921Z (about 2 months ago)
- Topics: cv2, deep-learning, keras, matplotlib-pyplot, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 38.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Shoe Classification using Convolutional Neural Network (CNN)
## Overview
**This project aims to classify different types of shoes using Convolutional Neural Networks (CNNs). The model is trained on a dataset of shoe images and is designed to accurately identify various shoe categories.**
**--The model classifies images of three types of footwear: boots, sandals, and slippers.**
**--Achieved an overall accuracy of 91% on the test set.**
## Precision, recall, and F1-scores for each class are as follows:
**a)Boots (0): Precision = 97%, Recall = 97%, F1-Score = 97%.**
**b)Sandals (1): Precision = 86%, Recall = 92%, F1-Score = 89%.**
**c)Slippers (2): Precision = 90%, Recall = 79%, F1-Score = 84%.**
**The model was trained and tested on a dataset of 156 images, with balanced precision and recall for most classes.**
***The purpose of this project is to develop a deep learning model capable of classifying different types of footwear, which can be used in e-commerce platforms or retail applications to automatically tag and categorize footwear images.***