MobileNet-v3-Small: Image Classification
MobileNet-V3-Small is the lightweight version in the third generation of the MobileNet series, designed for resource-constrained devices like mobile and edge devices. Based on automated machine learning (AutoML) techniques, MobileNet-V3-Small incorporates depthwise separable convolutions, a hard-swish activation function, and a Squeeze-and-Excitation (SE) attention mechanism to boost performance and efficiency. Compared to MobileNet-V3-Large, MobileNet-V3-Small has a more compact architecture with fewer parameters, making it suitable for efficient image classification, object detection, and other tasks in low-power environments. Common applications on mobile include real-time face recognition, gesture control, and image classification, delivering high accuracy with fast processing capabilities.
Source model
- Input shape: 224x224
- Number of parameters: 2.42M
- Model size: 9.71M
- Output shape: 1x1000
Source model repository: MobileNet-v3-Small
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE
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