GuitarVision – Revolutionizing card creation automation.

Context and Goal

For GuitarLand, a guitar distributor, we developed a unique solution that fully automates the process of creating product listings for marketplaces. The client needed to reduce the time and costs associated with creating thousands of listings, a process that previously took up to two months and required significant designer resources.

Success Criteria

Business metrics and operational KPI.
Data readiness and integration quality.
Security and compliance requirements.

Tasks

What needed to be solved and why it mattered for the business.

Guitar Recognition with Neural Networks

To train a neural network to recognize guitars in images, including guitar type, model, color, and position, you'll need to gather training data, label it, and then train an image classification model.

Background removal

Develop an algorithm for a neural network that can automatically cut out a guitar from a background. This requires training a segmentation model to separate the object (the guitar) from the background in an image.

Guitar mounting template.

Train the system to insert cut-out guitar images into card templates. The model should automatically adapt the background, position the guitar in the correct spot, and add the guitar's name and type to the card.

Integration and Interface

Integrate the system into client workflows to enable automated image and card processing. Additionally, develop an interface that allows employees to easily interact with the system, providing simple loading, configuration, and card output.

Employee training

Conduct training for the client's employees on how to use the system. It's important to teach them how to effectively use the interface, understand the settings, and correctly process cards using the automated system.

Solution Milestones

How we built delivery: from hypothesis to production.

1

Guitar Recognition with Neural Networks

A neural network has been developed to recognize guitars, trained to classify guitar types, their model, color, and position in an image. The model uses computer vision and deep learning techniques to accurately identify guitar characteristics based on image annotations.

2

Background removal

The background removal algorithm automatically cuts out the guitar from the image background using neural network segmentation. This results in a clean image of the guitar that can then be used in various card templates or for other purposes.

3

Guitar mounting template.

An algorithm has been developed that automatically inserts cut-out guitars into pre-prepared card templates. The system adapts the background, adjusts the guitar's position, and automatically adds the name and type of the guitar, enabling rapid creation of cards for marketplaces.

4

Integration and Interface

The system integrates with client workflows through a user-friendly API. An interface has been developed for uploading images, configuring templates, and automatically generating cards. The interface is intuitive and allows for easy management of the entire automation process.

5

Employee training

The client's employees have been trained on how to use the system. During the training, users became familiar with the interface features, learned how to configure settings and optimize processes for generating marketplace listings.

Results

Business impact validated by measurable outcomes.

Cost reduction

Expenses have decreased from 500,000 to 40,000 rubles.

Reducing time

The time it takes to create cards has been reduced from 2 months to 10 minutes.

Technology

Tools and engineering stack used in delivery.

Python

The primary programming language used to develop the system, train neural networks, and write automation scripts.

TensorFlow

TensorFlow was used to create and train neural networks that recognize guitar types, their models, colors, and positions in images. It was also used for image processing and automatic generation of cards.

OpenCV

OpenCV was used for preprocessing images, such as background removal and image enhancement, before feeding them into the neural network.

Flask

Flask was used to develop the web interface and API, which facilitated integration with the system and automated the card creation process.

PostgreSQL

PostgreSQL was used to store data about guitars, cards, and templates, which allowed for easy management of metadata and card generation.

FAQ

Answers to common questions about this case.

How does automated product listing creation for marketplaces work?

Automating marketplace product listing creation uses neural networks and machine learning to identify guitars, remove backgrounds, and generate listings. The system automatically inserts guitar images into pre-designed templates, adapts them to the model and color, and adds the product name and type. This significantly speeds up the process and reduces costs.

What technologies were used to automate the creation of cards?

Modern technologies like Python, TensorFlow for machine learning, OpenCV for image processing, and Flask for web interface integration, along with PostgreSQL for storing product data, were used to automate the creation of product cards. These technologies enable the rapid and accurate generation of cards for marketplaces.

How does a neural network recognize guitars in images?

The neural network is trained with TensorFlow to recognize guitar types, models, colors, and positions in images. It analyzes photos from various angles and automatically classifies the guitar, allowing for accurate placement in a marketplace listing.

How long does the card creation process take after automation?

After implementing the automated system, creating a product listing for the marketplace now takes only 10 minutes, whereas it previously took up to 2 months. This significantly speeds up the process of getting products onto marketplaces.

How do you integrate an automation system into business processes?

The automation system integrates with the client's existing business processes via an API, ensuring ease of use. A user-friendly web interface has been developed, allowing users to upload images, customize templates, and automatically generate cards.

How does the system help reduce card creation costs?

Automating the creation of cards significantly reduces costs. Previously, creating cards required expenses for designers and took a lot of time, costing the client 500,000 rubles. After implementing the system, expenses decreased to 40,000 rubles, resulting in substantial savings.

What are the advantages of automating marketplace listings?

The main advantages of automation are reduced time and costs, improved image accuracy and quality, and automatic generation of product listings that meet marketplace requirements. This improves work efficiency and speeds up the process of getting products online.