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Deformable Detr

Developed by SenseTime
Deformable DETR is an end-to-end object detection model that improves detection performance using Transformer architecture and deformable attention mechanisms.
Downloads 19.60k
Release Time : 3/2/2022

Model Overview

This model is trained on the COCO 2017 object detection dataset and can directly predict object categories and bounding boxes in images without complex post-processing steps.

Model Features

End-to-end object detection
Directly outputs detection results without complex post-processing steps
Deformable attention mechanism
Improves detection capability for small objects through deformable attention modules
Bipartite matching loss
Uses Hungarian algorithm for optimal matching between predictions and annotations

Model Capabilities

Image object detection
Multi-category recognition
Bounding box prediction

Use Cases

Computer vision
Scene understanding
Identify multiple objects and their locations in images
Can accurately detect common objects such as animals, vehicles, etc.
Intelligent surveillance
Real-time detection of specific targets in surveillance footage
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