C

Conditional Detr Resnet 101 Dc5

Developed by Omnifact
Conditional Detection Transformer (DETR) model that accelerates training convergence through conditional cross-attention mechanism, suitable for object detection tasks
Downloads 59
Release Time : 3/26/2024

Model Overview

This model is an object detection model based on the Conditional DETR architecture, using ResNet-101 as the backbone network and trained on the COCO dataset, capable of efficiently detecting objects in images and locating their bounding boxes.

Model Features

Fast training convergence
Uses conditional cross-attention mechanism, achieving 6.7-10x faster convergence speed compared to original DETR on R50/R101 backbone networks
Conditional spatial queries
Generates conditional spatial queries by learning decoder embeddings, enabling each attention head to focus on different regions and simplifying training difficulty
End-to-end detection
Directly outputs detection results and bounding boxes without complex post-processing

Model Capabilities

Image object detection
Multi-object recognition
Bounding box localization

Use Cases

General object detection
Everyday scene object detection
Detects common objects in daily environments such as homes and offices
Can accurately identify and locate objects like furniture and electronic devices
Outdoor scene analysis
Detects objects in natural or urban environments
Can recognize animals, vehicles, buildings, etc.
Surveillance and security
Public space monitoring
Detects people and items in places like airports and train stations
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