Overview / Usage

The Fashion Finder project is an image similarity application that employs k-nearest neighbors (KNN) to discover similar fashion products based on their image embeddings. The primary goal is to provide personalized product recommendations and enhance the shopping experience for customers in the fashion industry. By leveraging the KNN algorithm, the project efficiently finds products with similar styles, patterns, or designs, addressing the challenge of matching customers' preferences with relevant fashion items. It enables retailers to increase customer engagement, and ultimately improve customer satisfaction and conversion rates.

Methodology / Approach

The methodology of the Fashion Finder project revolves around utilizing advanced technologies and machine learning techniques to solve the problem of fashion product similarity and personalized recommendations. Here is an overview of the approach and technologies used:

1. Data Collection: The project begins by collecting a large dataset of fashion product images and their corresponding metadata. This data serves as the foundation for training and testing the model.
2. Data Preprocessing: The collected images are preprocessed to ensure uniformity and compatibility with the model. Techniques like resizing, normalization, and data augmentation are applied to enhance the model's performance.
3. Image Embeddings: To represent fashion product images in a numerical format, the project uses pre-trained deep learning models like VGG16 to generate image embeddings. These embeddings capture the underlying features and patterns of the images, making them suitable for similarity analysis.
4. K-Nearest Neighbors (KNN): The KNN algorithm is employed to find the most similar fashion products based on their image embeddings. When a new fashion product is inputted, the KNN algorithm searches for the k-nearest neighbors in the embedding space, effectively recommending products with similar visual characteristics.
5. Model Training: The KNN model is trained on the preprocessed data to ensure its effectiveness in finding relevant fashion product recommendations.

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Frameworks and Standards:

• TensorFlow and Keras: These deep learning frameworks are used for building and training the VGG16 model and the KNN classifier.
• ImageDataGenerator: This module from TensorFlow-Keras is employed for data augmentation during the image preprocessing phase.
• Pandas: Pandas is used for data manipulation and handling metadata in tabular form.
• Matplotlib and Seaborn: These libraries aid in data visualization and plotting fashion product images and similarity results.
• Sklearnex: sklearnex is an extension of scikit-learn that integrates Intel oneAPI concepts for efficient machine learning execution on CPUs and GPUs.

Technologies Used

Technologies, Libraries, Tools, and Software:

1. TensorFlow: A popular open-source deep learning framework used for building and training neural networks.
2. Keras: A high-level neural network API that runs on top of TensorFlow, facilitating model development and training.
3. Pandas: A powerful library for data manipulation and analysis, used for handling metadata in tabular form.
4. Matplotlib: A data visualization library used for creating various plots and graphs.
5. Seaborn: A statistical data visualization library built on top of Matplotlib, enhancing the visualization capabilities.
6. ImageDataGenerator: A module from TensorFlow-Keras for data augmentation during image preprocessing.
7. Sklearnex: It is an extension of scikit-learn that integrates Intel oneAPI concepts for efficient machine learning execution on CPUs and GPUs.
8. Jupyter Notebook: An interactive development environment that allows for code execution, visualization, and documentation in a web-based interface.
9. Python: The programming language used for developing the Fashion Finder project due to its versatility and rich libraries.

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Intel Technologies:

1. Intel oneAPI: A comprehensive software toolkit that provides developers with the tools, libraries, and frameworks to accelerate performance across a range of architectures, including CPUs, GPUs, and FPGAs.
2. Intel DevCloud: A cloud-based platform provided by Intel that offers access to various Intel hardware, including CPUs, GPUs, and FPGAs, for testing and development purposes.

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Overall, the Fashion Finder project leverages a range of technologies, libraries, tools, and potentially Intel hardware offerings to create an efficient and effective image similarity application.

Repository

https://github.com/santhoshpandiarajan/Fashion-Finder

Collaborators