RegNetQuantized: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

RegNet 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.

This model is an implementation of RegNetQuantized found here. This repository provides scripts to run RegNetQuantized on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

Model Type: Image classification
Model Stats:
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 15.3M
- Model size: 15.4 MB

Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	TFLite	0.898 ms	0 - 1 MB	INT8	NPU	RegNetQuantized.tflite
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	QNN Model Library	1.009 ms	0 - 61 MB	INT8	NPU	RegNetQuantized.so

Installation

This model can be installed as a Python package via pip.

pip install "qai-hub-models[regnet_quantized]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.regnet_quantized.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.regnet_quantized.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

Performance check on-device on a cloud-hosted device
Downloads compiled assets that can be deployed on-device for Android.
Accuracy check between PyTorch and on-device outputs.

python -m qai_hub_models.models.regnet_quantized.export

Profile Job summary of RegNetQuantized
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 1.01 ms
Estimated Peak Memory Range: 0.01-15.62 MB
Compute Units: NPU (113) | Total (113)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.regnet_quantized.demo --on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.regnet_quantized.demo -- --on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.
QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on RegNetQuantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of RegNetQuantized can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
For questions or feedback please reach out to us.

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RegNetQuantized