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Rexnet1 3x

Developed by frgfm
ReXNet-1.3x is an image classification model based on the ReXNet architecture, pretrained on the ImageNette dataset. The model reduces channel redundancy by improving the Squeeze-Excitation layers in residual blocks.
Downloads 15
Release Time : 3/2/2022

Model Overview

ReXNet-1.3x is an efficient convolutional neural network designed for image classification tasks. It enhances feature representation by optimizing the network architecture, making it suitable for various visual recognition scenarios.

Model Features

Optimized Residual Block Design
Prevents channel redundancy through customized Squeeze-Excitation layers, improving feature representation efficiency.
Lightweight Architecture
The 1.3x expanded version controls model complexity while maintaining performance.
Pretrained Model
Pretrained on the ImageNette dataset, ready for direct use in transfer learning.

Model Capabilities

Image Classification
Feature Extraction
Transfer Learning

Use Cases

Computer Vision
Object Recognition
Identify common object categories in images
Image Classification System
Build deep learning-based image classification applications
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