Swin-Base: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

SwinBase 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 Swin-Base found here.

This repository provides scripts to run Swin-Base on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

Model Type: Model_use_case.image_classification
Model Stats:
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 88.8M
- Model size (float): 339 MB
- Model size (w8a16): 90.2 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
Swin-Base	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	61.017 ms	0 - 356 MB	NPU	Swin-Base.tflite
Swin-Base	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	55.331 ms	1 - 314 MB	NPU	Swin-Base.dlc
Swin-Base	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	28.274 ms	0 - 350 MB	NPU	Swin-Base.tflite
Swin-Base	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	29.627 ms	1 - 336 MB	NPU	Swin-Base.dlc
Swin-Base	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	21.67 ms	0 - 33 MB	NPU	Swin-Base.tflite
Swin-Base	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	19.633 ms	0 - 57 MB	NPU	Swin-Base.dlc
Swin-Base	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	19.133 ms	0 - 62 MB	NPU	Swin-Base.onnx.zip
Swin-Base	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	25.077 ms	0 - 355 MB	NPU	Swin-Base.tflite
Swin-Base	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	22.649 ms	0 - 315 MB	NPU	Swin-Base.dlc
Swin-Base	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	61.017 ms	0 - 356 MB	NPU	Swin-Base.tflite
Swin-Base	float	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	55.331 ms	1 - 314 MB	NPU	Swin-Base.dlc
Swin-Base	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	21.677 ms	0 - 41 MB	NPU	Swin-Base.tflite
Swin-Base	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	19.703 ms	0 - 66 MB	NPU	Swin-Base.dlc
Swin-Base	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	31.906 ms	0 - 347 MB	NPU	Swin-Base.tflite
Swin-Base	float	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	29.853 ms	36 - 342 MB	NPU	Swin-Base.dlc
Swin-Base	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	21.816 ms	0 - 33 MB	NPU	Swin-Base.tflite
Swin-Base	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	19.575 ms	0 - 70 MB	NPU	Swin-Base.dlc
Swin-Base	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	25.077 ms	0 - 355 MB	NPU	Swin-Base.tflite
Swin-Base	float	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	22.649 ms	0 - 315 MB	NPU	Swin-Base.dlc
Swin-Base	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	15.037 ms	0 - 354 MB	NPU	Swin-Base.tflite
Swin-Base	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	13.498 ms	1 - 346 MB	NPU	Swin-Base.dlc
Swin-Base	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	12.924 ms	1 - 343 MB	NPU	Swin-Base.onnx.zip
Swin-Base	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	11.895 ms	0 - 355 MB	NPU	Swin-Base.tflite
Swin-Base	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	10.218 ms	1 - 313 MB	NPU	Swin-Base.dlc
Swin-Base	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	9.635 ms	1 - 314 MB	NPU	Swin-Base.onnx.zip
Swin-Base	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	10.164 ms	0 - 348 MB	NPU	Swin-Base.tflite
Swin-Base	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	8.144 ms	1 - 328 MB	NPU	Swin-Base.dlc
Swin-Base	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	8.104 ms	1 - 328 MB	NPU	Swin-Base.onnx.zip
Swin-Base	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	20.172 ms	1019 - 1019 MB	NPU	Swin-Base.dlc
Swin-Base	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	19.541 ms	175 - 175 MB	NPU	Swin-Base.onnx.zip
Swin-Base	w8a16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	39.535 ms	0 - 277 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	20.837 ms	0 - 85 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	20.981 ms	0 - 279 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	ONNX	552.304 ms	142 - 170 MB	CPU	Swin-Base.onnx.zip
Swin-Base	w8a16	RB5 (Proxy)	Qualcomm® QCS8250 (Proxy)	ONNX	620.883 ms	140 - 156 MB	CPU	Swin-Base.onnx.zip
Swin-Base	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	39.535 ms	0 - 277 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	20.942 ms	0 - 87 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	20.715 ms	0 - 84 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	20.981 ms	0 - 279 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	13.827 ms	0 - 283 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	10.808 ms	0 - 266 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	210.961 ms	93 - 166 MB	NPU	Swin-Base.onnx.zip
Swin-Base	w8a16	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	8.134 ms	0 - 273 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	21.72 ms	404 - 404 MB	NPU	Swin-Base.dlc
Swin-Base	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	186.699 ms	133 - 133 MB	NPU	Swin-Base.onnx.zip

Installation

Install the package via pip:

pip install qai-hub-models

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.swin_base.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.swin_base.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.swin_base.export

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.swin_base import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.swin_base.demo --eval-mode 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.swin_base.demo -- --eval-mode 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 Swin-Base's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Swin-Base 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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