KazanExpress - a trading platform with goods from Russia and China. Offers one day delivery
In 2020, EORA implemented its own MAGE image similarity search system on the KazanExpress website.
The customer decided to increase the accuracy of the search and gave us a new database of images: from stores and from users.
The system speeds up the purchase of goods: instead of using filters, the buyer simply uploads a photo.
Inside the service is a neural network that is able to analyze and find similar products.
Post training of the neural network on new photos
Increase the quality of the search for similar images
Speed up the service amid growing number of requests
Optimization of service performance
How it works
Buyer uploads a photo to search
The neural network accesses the database of the website and selects similar images
The customer sees a list of products with price tags and descriptions
Conversion from browsing to buying increases
«We did the project fast because we worked with an off-the-shelf product we know well: MAGE. We improved the search quality and also created a post-training module. It allows data to be indexed every time the image database is updated.»
API preparation to launch the model
Training a neural network on new photos
Data markup: selection of relevant images
minimum throughput of our computer model when running on a server
10 queries per second
This is how much the search accuracy has increased after post training the model
In the new database, there were many not relevant photos received from users. The search may have included a photo of the packaging, but not of the product itself. Such images were not suitable for training.
We conducted several experiments and selected the training method that gives the highest search accuracy. To post train and test the computer model, we used photos of the seller (store).
Kazan Express has its own data science team. It creates all the product features - with the exception of tools based on computer vision technology. Those are handled by EORA