Regnet Y 064
RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search
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Release Time : 3/18/2022
Model Overview
RegNet is an image classification model proposed by Facebook Research, optimized through neural architecture search (NAS) by designing search spaces. The model is trained on the ImageNet-1k dataset and is suitable for general image classification tasks.
Model Features
Neural Architecture Search Design
Automatically discovers efficient network architectures through systematic search space design and constraint optimization
Efficient Image Classification
Trained on ImageNet-1k, capable of accurately recognizing 1000 common object categories
Scalable Architecture
The model design method allows generating variants of different complexities to meet various needs
Model Capabilities
Image Classification
Object Recognition
Use Cases
Computer Vision
General Object Recognition
Recognize common objects in images such as animals, daily items, etc.
Can accurately identify 1000 ImageNet categories
Content Classification
Classify and organize image content
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