# Multi-category Recognition

This model is designed to classify news articles, helping users quickly identify and organize news content.

Vision Transformer (ViT)-based clothing image classification model with approximately 78% accuracy

This is a GLiNER model fine-tuned for identifying groceries and food categories in text, supporting recognition of 14 different types of groceries.

This is a fine-tuned model based on the DistilBERT architecture, specifically designed for product classification tasks in the e-commerce domain.

Food image classification model trained on the Food101 dataset with an accuracy of 88.31%

This is an image classification model generated by HuggingPics, capable of classifying various images such as animals, birds, and flags.

This is a multi-category image classification model trained via AutoTrain, capable of recognizing various common objects and scenes.

A Chinese news classification model trained using the Toutiao dataset based on the PyTorch framework, suitable for Chinese text classification learning scenarios.

This is a multi-category image classification model trained using AutoTrain, achieving an outstanding accuracy of 98.9% on the validation set.

This is a multi-category image classification model trained using AutoTrain, capable of recognizing various object categories.

An image classification model generated by HuggingPics for identifying specific categories of images

This is an image classification model generated by HuggingPics, capable of recognizing multiple categories of electronic devices.

An image classification model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the food101 dataset for food image classification tasks

This is a fine-tuned Indian food image classification model based on Google's ViT model, achieving an accuracy of 93.3%.

A RoBERTa-based QA classification model for identifying categories of non-factual questions

DiT is a Transformer-based document image classification model, pretrained on the IIT-CDIP dataset and fine-tuned on the RVL-CDIP dataset