# High-precision Detection

An object detection model based on the DETR architecture, suitable for general object detection tasks

YOLOE is a real-time visual omni-model that supports various vision tasks including zero-shot object detection.

This model is based on the DistilBERT architecture, specifically designed for multi-label classification tasks to determine whether emails and URLs are safe or pose phishing risks.

An object detection model based on YOLOv11, specifically designed for fall detection

A synthetic speech detection model fine-tuned based on MIT/ast-finetuned-audioset-10-10-0.4593, used to identify whether an audio is synthetic speech.

Florence-2 is an advanced vision foundation model developed by Microsoft, using a prompt-based approach to handle a wide range of vision and vision-language tasks.

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

RT-DETR is the first real-time end-to-end object detector, achieving a balance of speed and accuracy surpassing the YOLO series through efficient hybrid encoders and query selection mechanisms

RT-DETR is the first real-time end-to-end object detection Transformer model, achieving efficient NMS-free detection through a hybrid encoder and query selection mechanism

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.

This model is a classifier for detecting AI-generated images, achieving 97.36% accuracy on the evaluation set.

A model fine-tuned based on facebook/detr-resnet-50 for object detection tasks

A fine-tuned model based on facebook/detr-resnet-50, specific purpose not clearly stated

YOLOv8x model fine-tuned on Roboflow gesture dataset, specialized for gesture recognition tasks

A fine-tuned model based on facebook/detr-resnet-50 for object detection tasks