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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.

Qualcomm® QCS8550
QCS8550 (Proxy)
TorchScripttoQualcomm® AI Engine Direct
3.26ms
Inference Time
0-11MB
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

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 IoT Devices

  • QCS8250 (Proxy)
  • QCS8550 (Proxy)
  • RB5 (Proxy)

Supported IoT Chipsets

  • Qualcomm® QCS8250
  • Qualcomm® QCS8550