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Detr Resnet 50 Dc5 Panoptic

Developed by facebook
DETR is an end-to-end object detection model combining convolutional neural networks and Transformer architecture, supporting panoptic segmentation tasks.
Downloads 45
Release Time : 3/2/2022

Model Overview

This model adopts a Transformer encoder-decoder structure, achieving object detection through object query mechanisms, and can directly output class labels, bounding boxes, and segmentation masks.

Model Features

End-to-end training
Directly outputs detection results without complex hand-designed components (such as anchor boxes or non-maximum suppression).
Bipartite matching loss
Uses the Hungarian algorithm to establish optimal matching between predictions and annotations, combining cross-entropy and bounding box loss for optimization.
Multi-task support
Can be extended to a panoptic segmentation model by adding a mask head, simultaneously performing object detection and instance segmentation.

Model Capabilities

Object detection
Instance segmentation
Panoptic segmentation
Image analysis

Use Cases

Intelligent surveillance
Construction site safety monitoring
Detects workers, equipment, and hazardous areas on construction sites
Accurately identifies multiple targets in construction scenarios
Retail analytics
Product recognition
Automatically identifies and counts products on shelves
High detection accuracy for common products like apples and oranges
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