real-estate-image-classification
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT) |
| Accuracy | 89.58% |
| Framework | PyTorch |
| Author | andupets |
What is real-estate-image-classification?
This is a specialized image classification model designed for the real estate domain, created using HuggingPics. It can accurately classify different types of real estate images including bathrooms, bedrooms, dining rooms, house facades, kitchens, living rooms, and apartment facades. The model leverages Vision Transformer architecture and achieves an impressive accuracy of 89.58%.
Implementation Details
The model is implemented using PyTorch and incorporates Transformer architecture for image processing. It utilizes TensorBoard for visualization and monitoring, and includes Inference Endpoints for practical deployment.
- Built with HuggingPics framework for streamlined image classification
- Uses Vision Transformer (ViT) architecture
- Implements PyTorch backend for efficient processing
- Includes TensorBoard integration for performance monitoring
Core Capabilities
- Classification of 7 distinct real estate categories
- High accuracy classification of interior rooms
- Facade recognition for houses and apartments
- Optimized for real estate photography
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in real estate image classification with high accuracy and supports multiple room types and building facades. It's particularly notable for its use of Vision Transformer architecture and integration with modern ML tools.
Q: What are the recommended use cases?
The model is ideal for real estate platforms, property listing websites, and automated property classification systems. It can be used to automatically categorize property images, organize real estate portfolios, and streamline property listing processes.
