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Owlvit Tiny Non Contiguous Weight
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Owlvit Tiny Non Contiguous Weight

Developed by fxmarty
OWL-ViT is a vision Transformer-based open-vocabulary object detection model capable of detecting categories not present in the training dataset.
Text-to-Image
Transformers
Open Source License:MIT#Zero-shot visual recognition#Discontinuous weight testing#Multimodal alignment
Downloads 337
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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