https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset
My idea is to create a project that can provide fashion products similar to what customers demand, achieved by selecting an image from the dataset. It employs image processing and machine learning models to analyze and understand the visual content of images. Users can upload images, or select one from the dataset to find relevant images quickly and accurately. The system will process and index images based on various visual features, enabling efficient retrieval while considering factors like object recognition, color, composition, and context to deliver relevant search results. The interface will be user-friendly, allowing both casual users and professionals to find and explore images seamlessly.
How many times have we seen someone using a product and wished we had the same thing? Imagine how convenient it would be if we could easily find items on e-commerce websites by just uploading an image. This will reduce the time required to find desired items and also minimizes the frustration associated with the search process which contributes to enhancing the customer experience, and hence contributing to the increase in the rate of sales. This problem will be solved by my project the Fashion Product Similarity Finder.
The Fashion Product Similarity Finder addresses the challenge of efficiently locating relevant fashion products based on user preferences, visual content, and context. Key features include:
Image Processing and Feature Extraction;
Machine Learning Models like convolutional neural networks (CNNs), are utilized for feature extraction and representation learning;
Images are converted into vector representations using the learned features. These vectors capture the essence of each image's visual characteristics, enabling efficient comparisons between images;
To find visually similar products, the solution employs distance metrics like cosine similarity or Euclidean distance to compare the vector representations of different images. Images with closer vector representations are considered more similar;
The system is accessed through a user-friendly web interface built using a web development framework like Streamlit.
Tech Stack: The project involves a combination of several technologies and tools to achieve its goals:
Python programming language has been used.
PyTorch or TensorFlow: Used tools for creating and training neural network models, particularly CNNs for image analysis.
Streamlit: Used to develop the user-friendly web interface.
PIL (Python Imaging Library) or OpenCV: Libraries used for image processing tasks such as resizing, normalization, and basic image manipulations.
NumPy: Used for handling feature vectors and image data.
scipy.spatial.distance: Used for computing distances between feature vectors to determine image similarity.
Version Control: Tools like Git have been used for collaborative development and managing code versions.
By combining these technologies, I have created a comprehensive solution that offers efficient and accurate fashion product search capabilities for users, enhancing their experience and benefiting the e-commerce platform.