# Lightweight Classification

YOLOv5n-cls-v7.0 is a lightweight image classification model based on the YOLOv5 architecture, suitable for efficient visual task processing.

An image classification model based on the ReXNet architecture pre-trained on a wildfire binary classification dataset, utilizing a customized Squeeze-Excitation layer to eliminate channel redundancy issues

A ReXNet image classification model pre-trained on wildfire binary datasets, featuring improved residual block structures

This model is an image classification model fine-tuned on the MNIST dataset based on the Swin Transformer architecture, achieving an accuracy of 98.33%

This is an image classification model generated by HuggingPics, specifically designed to identify different types of grains.

ReXNet image classification model pretrained on ImageNette, utilizing improved residual block structures to reduce channel redundancy

An image classification model based on PyTorch and HuggingPics, specifically designed to identify five common types of flowers.