ResNet152: Imagenet classifier and general purpose backbone

ResNet152 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 is based on the implementation of ResNet152 found here. This is a standalone recipe compatible with the Qualcomm® AI Hub Models CLI — it can be installed, compiled, and evaluated on real Snapdragon devices via Qualcomm® AI Hub Workbench.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Use this recipe

Register it under a short name, then run it locally or compile it for a device:

qai-hub-models register my_model ashwmurt/resnet152
qai-hub-models demo my_model
qai-hub-models export my_model

Setup

1. Install the package

Install the base package, then use the qai-hub-models CLI to install this recipe's dependencies:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install qai-hub-models
qai-hub-models install resnet152

2. Configure Qualcomm® AI Hub Workbench

Sign-in to Qualcomm® AI Hub Workbench 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.

Run CLI Demo

Run the following simple CLI demo to verify the model is working end to end:

qai-hub-models demo resnet152

More details on the CLI tool can be found with the --help option. See demo.py for sample usage of the model including pre/post processing scripts.

By default, the demo will run locally in PyTorch. Pass --eval-mode on-device to run the model on a cloud-hosted target device.

Export for on-device deployment

To run the model on Qualcomm® devices, you must export the model for use with an edge runtime such as TensorFlow Lite, ONNX Runtime, or Qualcomm AI Engine Direct. Use the following command to export the model:

qai-hub-models export resnet152 --target-runtime tflite --precision float

Additional options are documented with the --help option.

License

• The license for the original implementation of ResNet152 can be found here.

References

• Deep Residual Learning for Image Recognition
• Source Model Implementation

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.