Regnet X 016
RegNet image classification model trained on ImageNet-1k, featuring an efficient network structure designed through neural architecture search
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Release Time : 3/15/2022
Model Overview
RegNet is an image classification model obtained through neural architecture search (NAS) by designing search spaces, proposed by Facebook Research. The model optimizes network architecture by progressively narrowing the search scope, suitable for general image classification tasks.
Model Features
Neural Architecture Search Design
Obtains optimal network structures through systematic search space design rather than manual design
Efficient Image Classification
Trained on the ImageNet-1k dataset, suitable for general image classification tasks
Scalable Architecture
The model design method allows for generating variants with different computational complexities
Model Capabilities
Image Classification
Visual Feature Extraction
Use Cases
General Image Recognition
Animal Recognition
Identify animal species in images
Successfully identified a tiger in the example
Object Recognition
Identify everyday objects such as teapots
Successfully identified a teapot in the example
Scene Recognition
Identify scene types such as buildings and landscapes
Successfully identified a palace in the example
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