R

Rexnet1 5x

Developed by frgfm
ReXNet-1.5x is a lightweight image classification model pretrained on the ImageNette dataset, utilizing the ReXNet architecture. It reduces channel redundancy by improving the Squeeze-Excitation layers within residual blocks.
Downloads 15
Release Time : 3/2/2022

Model Overview

This model is primarily used for image classification tasks, offering high efficiency and accuracy, making it suitable for resource-constrained environments.

Model Features

Improved Squeeze-Excitation Layer
Incorporates customized Squeeze-Excitation layers within residual blocks to effectively prevent channel redundancy and enhance model performance.
Lightweight Design
The model is designed to be lightweight, making it suitable for deployment and use in resource-constrained environments.
Efficient Inference
The model maintains high accuracy while delivering fast inference speeds.

Model Capabilities

Image Classification
Efficient Inference

Use Cases

Computer Vision
Image Classification
Used for classifying images, applicable to various visual recognition tasks.
Featured Recommended AI Models
ยฉ 2025AIbase