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ConvNext-Tiny-w8a16-Quantized

Imagenet classifier and general purpose backbone.

ConvNextTiny 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.

TorchScripttoQualcomm® AI Engine Direct
2.29ms
Inference Time
0-86MB
Memory Usage
215NPU
Layers

Technical Details

Model checkpoint:Imagenet
Input resolution:224x224
Number of parameters:28.6M
Model size:28 MB
Precision:w8a16 (8-bit weights, 16-bit activations)

Applicable Scenarios

  • Medical Imaging
  • Anomaly Detection
  • Inventory Management

Supported Mobile Form Factors

  • Phone
  • Tablet

Licenses

Source Model:BSD-3-CLAUSE
Deployable Model:AI Model Hub License

Tags

  • quantized
    A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

Supported Mobile Devices

  • Google Pixel 3
  • Google Pixel 3a
  • Google Pixel 3a XL
  • Google Pixel 4
  • Google Pixel 4a
  • Google Pixel 5a 5G
  • Samsung Galaxy S21
  • Samsung Galaxy S21 Ultra
  • Samsung Galaxy S21+
  • Samsung Galaxy S22 5G
  • Samsung Galaxy S22 Ultra 5G
  • Samsung Galaxy S22+ 5G
  • Samsung Galaxy S23
  • Samsung Galaxy S23 Ultra
  • Samsung Galaxy S23+
  • Samsung Galaxy S24
  • Samsung Galaxy S24 Ultra
  • Samsung Galaxy S24+
  • Samsung Galaxy Tab S8
  • Xiaomi 12
  • Xiaomi 12 Pro

Supported Mobile Chipsets

  • Snapdragon® 8 Gen 1 Mobile
  • Snapdragon® 8 Gen 2 Mobile
  • Snapdragon® 8 Gen 3 Mobile
  • Snapdragon® 888 Mobile