EfficientFormer is a vision transformer model that can classify images from the Imagenet dataset.
This is based on the implementation of EfficientFormer found here. This repository contains scripts for optimized on-device export suitable to run on Qualcomm® devices. More details on model performance across various devices, can be found here.
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.
Install the package via pip:
# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[efficientformer]"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_TOKENNavigate to docs for more information.
Run the following simple CLI demo to verify the model is working end to end:
python -m qai_hub_models.models.efficientformer.demo { --quantize w8a16 }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. Please refer to our general instructions on using
models for more usage instructions.
By default, the demo will run locally in PyTorch. Pass --eval-mode on-device to the demo script to run the model on a cloud-hosted target device.
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:
python -m qai_hub_models.models.efficientformer.export { --quantize w8a16 }Additional options are documented with the --help option.
- The license for the original implementation of EfficientFormer can be found here.
- 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.