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Regnet X 008

Developed by facebook
RegNet model trained on ImageNet-1k, an efficient image classification model designed via neural architecture search
Downloads 18
Release Time : 3/15/2022

Model Overview

RegNet is an image classification model proposed by Facebook Research, optimized through neural architecture search (NAS) with designed search spaces. The model is trained on the ImageNet-1k dataset and is suitable for general image classification tasks.

Model Features

Neural Architecture Search Optimization
Automatically optimizes model architecture through systematic search space design and constraint application
Efficient Image Classification
Efficient architecture optimized for large-scale image classification tasks like ImageNet
Scalable Design
Model design methodology allows for generating variants of different scales and complexities

Model Capabilities

Image Classification
Object Recognition
Visual Feature Extraction

Use Cases

General Image Recognition
Animal Recognition
Identify animal species in images
Correctly identifies tiger images in examples
Everyday Object Recognition
Identify common household items
Correctly identifies teapots in examples
Scene Recognition
Identify building and scene types
Correctly identifies palaces in examples
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