EfficientViT-b2-cls: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

EfficientViT 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 EfficientViT-b2-cls found here.

This repository provides scripts to run EfficientViT-b2-cls 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: 24.3M
- Model size (float): 92.9 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
EfficientViT-b2-cls	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	12.155 ms	0 - 102 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	12.846 ms	1 - 62 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	6.399 ms	0 - 113 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	6.882 ms	1 - 69 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	4.981 ms	0 - 345 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	5.344 ms	0 - 15 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	5.328 ms	0 - 123 MB	NPU	EfficientViT-b2-cls.onnx.zip
EfficientViT-b2-cls	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	6.096 ms	0 - 103 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	6.635 ms	1 - 62 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	TFLITE	5.025 ms	0 - 343 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN_DLC	5.405 ms	0 - 15 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	ONNX	5.347 ms	0 - 66 MB	NPU	EfficientViT-b2-cls.onnx.zip
EfficientViT-b2-cls	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	3.425 ms	0 - 116 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	3.767 ms	1 - 76 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	3.717 ms	0 - 76 MB	NPU	EfficientViT-b2-cls.onnx.zip
EfficientViT-b2-cls	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	TFLITE	3.332 ms	0 - 104 MB	NPU	EfficientViT-b2-cls.tflite
EfficientViT-b2-cls	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN_DLC	3.263 ms	0 - 65 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	ONNX	2.891 ms	0 - 63 MB	NPU	EfficientViT-b2-cls.onnx.zip
EfficientViT-b2-cls	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	6.043 ms	286 - 286 MB	NPU	EfficientViT-b2-cls.dlc
EfficientViT-b2-cls	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	5.814 ms	49 - 49 MB	NPU	EfficientViT-b2-cls.onnx.zip

Installation

Install the package via pip:

pip install "qai-hub-models[efficientvit-b2-cls]"

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.efficientvit_b2_cls.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.efficientvit_b2_cls.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.efficientvit_b2_cls.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.efficientvit_b2_cls import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# 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.efficientvit_b2_cls.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.efficientvit_b2_cls.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 EfficientViT-b2-cls's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of EfficientViT-b2-cls 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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