Shufflenet-v2Quantized
Imagenet classifier and general purpose backbone.
ShufflenetV2 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
Technical Details
Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:1.37M
Model size:4.42 MB
Applicable Scenarios
- Medical Imaging
- Anomaly Detection
- Inventory Management
Licenses
Source Model:BSD-3-CLAUSE
Deployable Model:AI Model Hub License
Tags
- quantizedA “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.
Supported Compute Chipsets
- Snapdragon® X Elite