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Mit Indoor Scenes
Image classification model based on Vision Transformer architecture, pre-trained on ImageNet-21k dataset and fine-tuned with MIT indoor scene dataset
Downloads 14
Release Time : 3/7/2022
Model Overview
This model uses the Vision Transformer architecture, specifically designed for image classification tasks, with optimizations for indoor scene recognition.
Model Features
Transformer-based vision model
Applies the successful Transformer architecture from natural language processing to computer vision tasks
Large-scale pre-training
Pre-trained on ImageNet-21k dataset containing 14 million images and 21,000 categories
Domain-specific fine-tuning
Fine-tuned on MIT indoor scene dataset to optimize indoor scene recognition capabilities
Efficient image processing
Uses 16x16 image patches as input to balance computational efficiency and model performance
Model Capabilities
Image classification
Scene recognition
Indoor environment analysis
Use Cases
Smart home
Room type identification
Automatically identifies room types from camera footage (bedroom, kitchen, living room, etc.)
Can be used for automatic scene configuration in smart home systems
Real estate
Property photo classification
Automatically classifies room types in property photos
Improves photo management efficiency for real estate platforms
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