Owlvit Tiny Non Contiguous Weight
OWL-ViT is a vision Transformer-based open-vocabulary object detection model capable of detecting categories not present in the training dataset.
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Release Time : 1/16/2024
Model Overview
OWL-ViT combines vision Transformer and text encoder to enable real-time object detection in images through text descriptions without requiring training for specific categories.
Model Features
Zero-shot detection
Detects new objects without requiring training for specific categories
Multimodal understanding
Processes both visual and textual inputs for semantic alignment
Efficient architecture
Lightweight design based on Vision Transformer
Model Capabilities
Open-vocabulary object detection
Image-text alignment
Zero-shot learning
Multimodal reasoning
Use Cases
Intelligent surveillance
Anomalous object detection
Real-time detection of anomalous objects in surveillance footage through text descriptions
Can identify dangerous items not seen during training
Retail analytics
Product recognition
Identifies newly stocked products without retraining
Reduces maintenance costs for product recognition systems
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