Helmet Detection

A table detection model based on the DETR architecture, specifically designed for extracting tables from unstructured documents

Grounding DINO is an open-set object detection model that achieves zero-shot object detection capabilities by combining the DINO detector with a text encoder.

Grounding DINO is an open-set object detection model that combines the DINO detector with grounding pre-training, enabling zero-shot object detection.

DETR is an end-to-end object detection model based on Transformer architecture, using ResNet-50 as the backbone network and trained on the COCO dataset.

DETR is an end-to-end object detection model using Transformer architecture, employing ResNet-101 as the backbone network and trained on the COCO dataset.

Document table detection model based on DETR architecture for detecting bordered and borderless tables in documents

A vision Transformer (ViT)-based object detection model trained with DETR loss function, achieving excellent performance on the COCO dataset.

YOLOS model fine-tuned on the COCO 2017 object detection dataset, utilizing Vision Transformer architecture for efficient object detection.

RT-DETR is the first real-time end-to-end object detector, achieving 53.1% AP and 108 FPS on the COCO dataset through an efficient hybrid encoder and uncertainty-minimized query selection mechanism.

The first real-time end-to-end object detector based on Transformer architecture, eliminating the need for non-maximum suppression, surpassing YOLO series in speed and accuracy