# High-precision detection

RT-DETRv2 is an improved real-time object detection Transformer model that enhances performance through strategies such as selective multi-scale feature extraction and dynamic data augmentation.

RT-DETRv2 is an improved real-time detection transformer that enhances flexibility and practicality through plug-and-play optimization schemes and achieves stronger performance with optimized training strategies.

VisualHeist is an object detection model specifically designed to extract charts, schematics, and tables from PDF files, including titles, headers, and footers.

This model is built based on DistilBERT. After fine-tuning, it can perform multi-label classification on emails and URLs to determine whether they are safe or have potential phishing risks.

Florence-2 is an advanced visual foundation model developed by Microsoft, employing a prompt-based architecture to unify various vision and vision-language tasks

TF-ID is a series of fine-tuned object detection models used to extract tables and images from academic papers, solving the problem of automatic recognition of tables and images in academic papers.

Florence-2 is an advanced vision foundation model developed by Microsoft, employing a prompt-based approach to handle various vision and vision-language tasks.

A violence detection model optimized based on the Vision Transformer (ViT) architecture, capable of classifying images into violent or non-violent scenes.

RT-DETR is the first real-time end-to-end object detector, achieving efficient detection through hybrid encoders and query selection mechanisms without NMS post-processing.

YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, with significant improvements in both speed and accuracy.

YOLOv10 is an efficient real-time object detection model without extra training costs. By optimizing architecture and training strategies, it improves detection accuracy while maintaining real-time performance.

RT-DETR is the first real-time end-to-end object detection Transformer model, achieving high-speed and high-precision detection through an efficient hybrid encoder and query selection mechanism

A lightweight model based on MobileBERT for detecting SQL injection attacks in input strings, suitable for edge computing scenarios.

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.

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