Property classification

Precise classification of property-related images across various industries.

Overview

Efficient categorization of digital content is essential for organizing and retrieving assets effectively. The Property classification model is an advanced machine learning (ML) solution designed to identify and classify the type of real estate property depicted within an image.

The model analyzes the input image and determines if the displayed realty falls into one of the following property classes:

• Apartment

• Bathroom

• Bedroom

• Dining

• Garage

• House

• Industrial

• Kitchen

• Living

• Office

The Property classification feature is based on state-of-the-art computer vision techniques and deep learning algorithms that interpret the image data and identify patterns that are indicative of the property type depicted. This allows the model to accurately predict the predefined category that the image is part of, enabling precise classification. By doing this, this model significantly enhances the management of digital assets, streamlining content organization and retrieval for various industries.

Typical use cases

Property classification finds utility in a variety of use cases, all aimed at improving digital asset management and content organization:

• Real estate listings - Real estate agencies can use the model to automatically classify and organize property images for listing websites, making it easier for potential buyers or renters to find properties of interest.

• Property management - Property management companies can organize images of rental properties, simplifying property management and tenant communication.

• Interior design - Interior design platforms can categorize images of furniture and home decoration products, thus enhancing the online shopping experience for customers.

• Hospitality industry - The model can help hotels and hospitality businesses sort images of their rooms, facilities and dining areas for marketing and reservation platforms.

• E-commerce product classification - E-commerce platforms can use the model to automatically categorize products based on the type of setting they are intended for (e.g., kitchen appliances, bedroom furniture).

API endpoints

Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:

Scaleflex API for Digital Asset Management (DAM), Visual AI and Media OptimizationScaleflex API

There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.

Example API responses

Input image	API response
	{ "status": "success", "version": "3.0.2", "predictions": [ { "bedroom": 1, "apartment": 0, "kitchen": 0 } ], "request_uuid": "a044541f-d996-4231-8a06-f68754b017a4", "sha1": "c8f52a7c740d48bc1fa428655ff2289940615c3e" }
	{ "status": "success", "version": "3.0.2", "predictions": [ { "apartment": 0.91, "office": 0.07, "dinner": 0.01 } ], "request_uuid": "6aa97ee2-02cc-40cc-a228-78132855c88a", "sha1": "aa98fea5e0cd0aec274c07ec5bb7633b1d37c227" }
	{ "status": "success", "version": "3.0.2", "predictions": [ { "kitchen": 1, "living": 0, "bathroom": 0 } ], "request_uuid": "520fc7e2-ee42-4961-9f22-0774bac40ea2", "sha1": "a5b0c6c8e481c4eb9410d5790a3e6ed68d28b525" }