# Multi-category recognition

A food image classification model fine-tuned from google/siglip2-base-patch16-224, capable of recognizing 101 popular dishes

A fine-tuned version of the BLIP model for the visual question-answering task on retail product images, fine-tuned on a custom dataset annotated with images and product descriptions from online retail platforms.

DETR is a Transformer-based object detection model that handles detection tasks in an end-to-end manner, eliminating the need for complex post-processing steps.

A multi-category image classification model trained on AutoTrain, focusing on solar panel object detection tasks

A general-purpose image classification model capable of recognizing and classifying objects in input images

This is a multi-category image classification model trained using AutoTrain, excelling on bicycle-related datasets.

A multi-category image classification model trained using AutoTrain, demonstrating outstanding performance on test datasets

This is a multi-category image classification model trained via AutoTrain, demonstrating outstanding performance on the validation set with an accuracy of 97.4%.

Semantic segmentation model fine-tuned from nvidia/mit-b0 for building facade image segmentation tasks

This is an image classification model generated by HuggingPics, primarily used for classifying images of specific types (genres).

This is an image classification model generated by HuggingPics, achieving an accuracy rate of 94.12%.

This is a multi-category image classification model trained using AutoTrain, demonstrating outstanding performance on the validation set with an accuracy of 94.1%.

This is a multi-category image classification model trained using AutoTrain, based on the ConvNeXt architecture, suitable for general image classification tasks.

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

This is a high-performance image classification model trained via AutoTrain, achieving 99.8% accuracy on the validation set.

YOLOv5s is an efficient lightweight version of the YOLO series object detection model, implemented based on PyTorch, suitable for real-time object detection tasks.

This is a multi-category image classification model trained by AutoTrain, specifically designed for analyzing vehicle images.

An image classification model based on the Vision Transformer architecture, fine-tuned on the CIFAR-100 dataset with an accuracy of 91.48%

A text classification model fine-tuned on the dbpedia_14 dataset based on bert-base-uncased, achieving 99.03% accuracy