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VIT

Image Classification
PyTorch
LiteRT
ONNX
backbone
android
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VIT: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

VIT 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 VIT found here.

This repository provides scripts to run VIT 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: 86.6M
    • Model size: 330 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
VIT float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 44.359 ms 0 - 279 MB NPU VIT.tflite
VIT float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 45.507 ms 1 - 10 MB NPU Use Export Script
VIT float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 18.906 ms 0 - 290 MB NPU VIT.tflite
VIT float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 21.572 ms 1 - 304 MB NPU Use Export Script
VIT float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 12.749 ms 0 - 17 MB NPU VIT.tflite
VIT float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 13.911 ms 1 - 2 MB NPU Use Export Script
VIT float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 16.104 ms 0 - 279 MB NPU VIT.tflite
VIT float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 16.864 ms 1 - 10 MB NPU Use Export Script
VIT float SA7255P ADP Qualcomm® SA7255P TFLITE 44.359 ms 0 - 279 MB NPU VIT.tflite
VIT float SA7255P ADP Qualcomm® SA7255P QNN 45.507 ms 1 - 10 MB NPU Use Export Script
VIT float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 12.83 ms 0 - 17 MB NPU VIT.tflite
VIT float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 13.842 ms 1 - 3 MB NPU Use Export Script
VIT float SA8295P ADP Qualcomm® SA8295P TFLITE 21.031 ms 0 - 280 MB NPU VIT.tflite
VIT float SA8295P ADP Qualcomm® SA8295P QNN 20.434 ms 1 - 18 MB NPU Use Export Script
VIT float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 13.14 ms 0 - 27 MB NPU VIT.tflite
VIT float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 13.923 ms 1 - 3 MB NPU Use Export Script
VIT float SA8775P ADP Qualcomm® SA8775P TFLITE 16.104 ms 0 - 279 MB NPU VIT.tflite
VIT float SA8775P ADP Qualcomm® SA8775P QNN 16.864 ms 1 - 10 MB NPU Use Export Script
VIT float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 12.841 ms 0 - 16 MB NPU VIT.tflite
VIT float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 13.896 ms 0 - 30 MB NPU Use Export Script
VIT float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 13.507 ms 0 - 325 MB NPU VIT.onnx
VIT float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 9.06 ms 0 - 286 MB NPU VIT.tflite
VIT float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 9.686 ms 1 - 309 MB NPU Use Export Script
VIT float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 9.516 ms 1 - 308 MB NPU VIT.onnx
VIT float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 8.299 ms 0 - 282 MB NPU VIT.tflite
VIT float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 7.913 ms 1 - 284 MB NPU Use Export Script
VIT float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 6.804 ms 0 - 287 MB NPU VIT.onnx
VIT float Snapdragon X Elite CRD Snapdragon® X Elite QNN 14.776 ms 1 - 1 MB NPU Use Export Script
VIT float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 14.917 ms 171 - 171 MB NPU VIT.onnx
VIT w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 18.328 ms 0 - 46 MB NPU VIT.tflite
VIT w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 30.6 ms 0 - 10 MB NPU Use Export Script
VIT w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 14.035 ms 0 - 56 MB NPU VIT.tflite
VIT w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 14.884 ms 0 - 158 MB NPU Use Export Script
VIT w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 8.78 ms 0 - 28 MB NPU VIT.tflite
VIT w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 10.68 ms 0 - 3 MB NPU Use Export Script
VIT w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 9.158 ms 0 - 47 MB NPU VIT.tflite
VIT w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 9.697 ms 0 - 12 MB NPU Use Export Script
VIT w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 56.087 ms 2 - 40 MB NPU VIT.tflite
VIT w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 78.803 ms 0 - 12 MB NPU Use Export Script
VIT w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 18.328 ms 0 - 46 MB NPU VIT.tflite
VIT w8a8 SA7255P ADP Qualcomm® SA7255P QNN 30.6 ms 0 - 10 MB NPU Use Export Script
VIT w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 8.906 ms 0 - 26 MB NPU VIT.tflite
VIT w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 10.805 ms 0 - 3 MB NPU Use Export Script
VIT w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 15.98 ms 0 - 50 MB NPU VIT.tflite
VIT w8a8 SA8295P ADP Qualcomm® SA8295P QNN 16.872 ms 0 - 18 MB NPU Use Export Script
VIT w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 8.889 ms 0 - 11 MB NPU VIT.tflite
VIT w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 10.719 ms 0 - 6 MB NPU Use Export Script
VIT w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 9.158 ms 0 - 47 MB NPU VIT.tflite
VIT w8a8 SA8775P ADP Qualcomm® SA8775P QNN 9.697 ms 0 - 12 MB NPU Use Export Script
VIT w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 8.817 ms 0 - 20 MB NPU VIT.tflite
VIT w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 10.696 ms 0 - 28 MB NPU Use Export Script
VIT w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 25.224 ms 0 - 117 MB NPU VIT.onnx
VIT w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 6.308 ms 0 - 52 MB NPU VIT.tflite
VIT w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 7.147 ms 0 - 151 MB NPU Use Export Script
VIT w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 17.736 ms 0 - 176 MB NPU VIT.onnx
VIT w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 5.892 ms 0 - 50 MB NPU VIT.tflite
VIT w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 5.81 ms 0 - 148 MB NPU Use Export Script
VIT w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 17.163 ms 0 - 169 MB NPU VIT.onnx
VIT w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 11.34 ms 0 - 0 MB NPU Use Export Script
VIT w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 29.213 ms 88 - 88 MB NPU VIT.onnx

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.vit.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.vit.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.vit.export

Profiling Results
------------------------------------------------------------
VIT
Device                          : cs_8275 (ANDROID 14)                  
Runtime                         : TFLITE                                
Estimated inference time (ms)   : 44.4                                  
Estimated peak memory usage (MB): [0, 279]                              
Total # Ops                     : 1579                                  
Compute Unit(s)                 : npu (1579 ops) gpu (0 ops) cpu (0 ops)

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.vit 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.vit.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.vit.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 VIT's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

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