real-estate-image-classification-30classes
| Property | Value |
|---|---|
| Architecture | Vision Transformer (ViT) |
| Framework | PyTorch |
| Accuracy | 66.67% |
| Categories | 30 real estate classes |
What is real-estate-image-classification-30classes?
This is a specialized image classification model designed to categorize real estate-related images across 30 distinct classes. Created using HuggingPics, it leverages Vision Transformer architecture to identify various property spaces and features ranging from basic rooms like bedrooms and bathrooms to amenities such as barbecue areas, saunas, and security facilities.
Implementation Details
The model is implemented using PyTorch and utilizes the Vision Transformer (ViT) architecture. It includes TensorBoard integration for monitoring and evaluation, and supports inference endpoints for practical deployment. The model achieves a 66.67% accuracy rate on classification tasks.
- Built on PyTorch framework with Transformer architecture
- Supports 30 distinct real estate categories
- Includes TensorBoard integration
- Provides inference endpoints for deployment
Core Capabilities
- Classification of interior spaces (bedrooms, bathrooms, kitchens, etc.)
- Identification of amenity areas (gym, sauna, barbecue)
- Recognition of exterior features (facade, gate, yard)
- Detection of specialized spaces (coworking rooms, beauty salons)
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in real estate-specific image classification with an extensive range of 30 classes, making it particularly valuable for property listing platforms and real estate applications. Its use of Vision Transformer architecture provides robust feature recognition capabilities.
Q: What are the recommended use cases?
The model is ideal for automatic categorization of real estate photographs, property listing management systems, virtual tour applications, and real estate inventory management. It can help automate the process of organizing and labeling property images at scale.
