Visual Search in E-commerce

Visual product search using AI allows users to find products by uploading images instead of typing a text query.

Visual search within e-commerce leveraging artificial intelligence enables customers to locate products by uploading images, moving beyond traditional textual searches. From computer vision to image embeddings, through similarity search and recommendations – AI significantly enhances the product discovery experience via visual search.

What is it: Image Vector Representations

Methods: ResNet, CLIP, image encoders

AI role: Generation of embeddings

live demo · related simulation● LIVE

What is it: Attribute Extraction

Methods: Object detection, classification

AI role: Identifying product characteristics

Frequently asked questions

How does a user initiate visual search?

A user uploads an image, and AI creates an embedding of that image through a computer vision model. Then, it finds the most similar embeddings of products within a vector space using similarity search.

Which models are typically used for visual search?

ResNet, EfficientNet are often utilized for creating basic image embeddings, while CLIP is frequently employed for text-image matching and Vision Transformers for high-quality results. Fine-tuning on your specific data can improve performance.

What types of models are used for generating initial image embeddings?

ResNet and EfficientNet are common choices for creating foundational image embeddings, while CLIP excels at text-image matching and Vision Transformers deliver high-quality results. Fine-tuning these models on your own dataset can significantly enhance their accuracy.

How can I optimize the performance of my visual search system?

Optimizing visual search involves carefully selecting appropriate image embeddings, tuning similarity search parameters and continuously evaluating and retraining your model with new data to ensure it remains accurate and relevant.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation