# High-precision Recognition

A fine-tuned model based on RoBERTa-base for detecting AI-generated English text.

This is a dataset for phishing attack detection, primarily used to train BERT models to identify phishing websites.

This is a fine-tuned object detection model based on the YOLOv3 architecture, optimized for specific scenario recognition tasks

A plant identification model fine-tuned based on Google Vision Transformer (ViT) architecture, achieving 80.96% accuracy on the evaluation set

A fine-tuned version based on MCG-NJU/videomae-large model on the deepfake detection challenge dataset, used for video deepfake detection.

YOLOv10 is a real-time object detection model that achieves efficient and overhead-free object detection by eliminating post-processing steps such as Non-Maximum Suppression (NMS).

A face detection model based on the CreativeML-OpenRAIL-M license, supporting the English language, primarily used for object detection tasks.

An example vision model for recognizing vehicle license plates, capable of extracting license plate numbers from images.

A multilingual detection model based on XLM-RoBERTa, capable of identifying the language category of text.

An AI model specialized in Chinese invoice recognition, capable of accurately extracting key information from invoices.

This model is a fine-tuned version of facebook/convnextv2-large-22k-224 on a dog breed classification dataset, achieving an accuracy of 91.39% on the evaluation set.

This model is a vision Transformer model fine-tuned on a fashion image gender and age classification dataset based on Google's ViT-Large architecture, achieving 99.6% accuracy on the evaluation set.

A plant image classification model based on the ViT architecture, achieving 99.95% validation accuracy after fine-tuning on an unknown dataset

End-to-end object detection model based on Transformer architecture with ResNet-101 feature extractor

A visual model for plant leaf condition classification, capable of accurately identifying and analyzing the health status of plant leaves.

Food classification model fine-tuned on the food101 dataset based on Google's ViT model

Food image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on the Food101 dataset with an accuracy of 90.9%

Food image classification model based on ViT architecture, fine-tuned on the Food101 dataset with an accuracy of 89.7%

A fine-tuned Vision Transformer model based on google/vit-base-patch16-224-in21k, achieving 70% accuracy on an unknown dataset

This is an image classification model based on the Vision Transformer (ViT) architecture, specifically designed for legume recognition tasks.

Image classification model fine-tuned on the herbier_mesuem5 dataset based on google/vit-base-patch16-224-in21k

An image classification model fine-tuned on the Food101 dataset based on the Swin Transformer architecture, achieving an accuracy of 92.14%

This model is a fine-tuned version based on microsoft/layoutlmv2-base-uncased, specializing in document information extraction tasks, excelling in extracting fields such as Aadhaar card numbers, date of birth, gender, and names.

A food image classification model fine-tuned based on the Swin Transformer architecture, achieving 92.1% accuracy on the Food101 dataset

An image classification model fine-tuned on the beans dataset based on the ResNet-50 architecture, achieving an accuracy of 90.23%

A lightweight image classification model based on Microsoft's Swin Transformer Tiny architecture, achieving 92.86% test accuracy after fine-tuning on a snack classification dataset