Open-source Deta-ResNet-50 Detection Model - Achieve Rapid Convergence for Training and Testing, Efficiently Implement Object Detection

Deta Resnet 50

Developed by jozhang97

DETA reintroduces IoU assignment and non-maximum suppression for transformer-based detectors, achieving comparable training and testing speeds to Deformable DETR while significantly faster convergence.

Object Detection

Transformers

#Fast Convergence Detection #IoU Assignment #Non-Maximum Suppression

Downloads 661

Release Time : 12/21/2022

Model Overview

DETA optimizes the training efficiency and performance of transformer-based detectors by reintroducing IoU assignment and non-maximum suppression.

Model Features

Fast Convergence

Achieves 50.2 mAP on COCO dataset in just 12 epochs, with significantly faster convergence than other models.

Efficient Training & Testing

Maintains comparable training and testing speeds to Deformable DETR.

IoU Assignment

Reintroduces IoU assignment to optimize detector performance.

Non-Maximum Suppression

Reintroduces non-maximum suppression to further improve detection accuracy.

Model Capabilities

Object Detection

Image Analysis

Use Cases

Computer Vision

Object Detection

Used for detecting multiple objects in images.

Achieves 50.2 mAP on COCO dataset.

Property	Details
Pipeline Tag	object - detection
Tags	vision

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Deta Resnet 50

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Detection Transformers with Assignment

🚀 Quick Start

Key Information

Summary