AI-Fashion-Image-Classification

Table of Contents

1. Project Overview
2. Business Understanding
3. Business Problem
4. Stakeholders
5. Data Description
6. Objectives
   • Main Objectives
   • Key Business Questions
7. Methodology
8. Results and Findings
9. Conclusion
   • Findings
   • Recommendations
10. Future Work
11. How to Use This Repository
12. Requirements

Project Structure

1. Notebook
2. Notebook PDF
3. Power Point Presentation
4. Presentation PDF
5. Requirements File
6. Read Me
7. Streamlit App
8. LICENSE

Project Overview

This project aims to develop an AI-based model for fashion image classification, addressing the growing need for efficient and accurate classification systems in the fashion industry. By leveraging advanced machine learning techniques, we aim to enhance the design, marketing, and customer experience in fashion e-commerce and retail.

Business Understanding

The fashion industry is characterized by rapid changes in consumer preferences and trends, necessitating a robust mechanism for image classification. Major fashion brands have recognized the need for more precise AI models to optimize their design processes and consumer interactions. This project seeks to fill this gap by developing a model that can accurately classify fashion images.

Business Problem

Despite advancements in AI, existing fashion image classification models often lack the precision required for real-world applications. This leads to inefficiencies in design and marketing strategies, resulting in misaligned inventory, poor customer experiences, and lost sales opportunities. The project addresses the need for a more effective classification model that can be utilized across various fashion industry applications.

Stakeholders

Key stakeholders include:

• Fashion designers
• E-commerce platforms
• Retailers
• Consumers
• Industry leaders and creative directors from major fashion houses
• Technology developers

Data Description

The project utilizes the Fashion-MNIST dataset, which comprises 70,000 grayscale images of clothing items, with 60,000 images designated for training and 10,000 for testing. Each image is flattened into a feature vector of 784 features, allowing for effective model training. The dataset includes various clothing categories such as dresses, coats, sandals, shirts, sneakers, bags, and ankle boots.

Objectives

Main Objectives

• Develop a robust AI model for classifying clothing items.
• Achieve high accuracy in classification to support fashion e-commerce and retail.
• Explore advanced model architectures to improve performance.
• Implement real-time testing and deployment of the model.

Key Business Questions

1. How can AI-driven fashion image classification improve operational efficiency in design and marketing?
2. What machine learning techniques yield the highest accuracy for fashion image classification?
3. How can the developed model be integrated into existing fashion industry workflows?
4. What are the user experiences and feedback from stakeholders using the interactive application?
5. How can the model be adapted to cater to the unique needs of the Kenyan fashion market?

Methodology

The methodology involves the following steps:

1. Data Collection: Gather diverse clothing images from various sources.
2. Data Preprocessing: Normalize and resize images for uniformity.
3. Model Development: Utilize a Convolutional Neural Network (CNN) architecture for classification.
4. Training and Validation: Split the dataset into training, validation, and test sets, and train the model using the training set.
5. Evaluation: Assess the model's performance using accuracy, precision, recall, and F1-score metrics.

Results and Findings

The model achieves an overall accuracy of 90%, indicating strong performance in classifying clothing categories. However, the precision and recall for certain categories, such as "Shirt," are lower, suggesting room for improvement. The confusion matrix highlights specific challenges, such as confusion between "Shirt" and "T-shirt/top."

Conclusion

Findings

• The model demonstrates high accuracy across most clothing categories.
• Certain categories exhibit lower precision and recall, indicating potential misclassifications.
• The classification report shows a balanced performance across classes, with a macro average F1-score of 0.90.

Recommendations

• Explore more advanced architectures, such as transfer learning models, to enhance classification performance.
• Incorporate additional data to improve the model's robustness and generalization capabilities.
• Develop a feedback loop for continuous improvement based on user interactions.

Future Work

• Implement real-time testing and deployment of the model to evaluate its performance in practical applications, such as fashion recommendation systems.
• Utilize cross-validation techniques to ensure the model's performance is consistent across different subsets of the data.
• Investigate user feedback integration to continuously improve the model's accuracy over time.

How to Use This Repository

To set up the project, follow these steps:

1. Clone the repository:

   git clone [email protected]:iamisaackn/AI-Fashion-Image-Classification.git
   cd fashion-image-classification

2. Install the required packages:

   pip install -r requirements.txt

3. Run the model:

   python main.py

Requirements

• Python 3.x
• TensorFlow
• Keras
• NumPy
• Matplotlib
• OpenCV
• Scikit-learn