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WideResNet50

Image Classification
PyTorch
LiteRT
backbone
android
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WideResNet50: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

WideResNet50 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 WideResNet50 found here.

This repository provides scripts to run WideResNet50 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: 68.9M
    • Model size (float): 263 MB
    • Model size (w8a8): 66.6 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
WideResNet50 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 23.999 ms 0 - 92 MB NPU WideResNet50.tflite
WideResNet50 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 24.375 ms 1 - 41 MB NPU WideResNet50.dlc
WideResNet50 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 6.824 ms 0 - 175 MB NPU WideResNet50.tflite
WideResNet50 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 9.193 ms 0 - 38 MB NPU WideResNet50.dlc
WideResNet50 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 4.86 ms 0 - 850 MB NPU WideResNet50.tflite
WideResNet50 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 4.756 ms 3 - 14 MB NPU WideResNet50.dlc
WideResNet50 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 4.781 ms 0 - 167 MB NPU WideResNet50.onnx.zip
WideResNet50 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 7.318 ms 0 - 92 MB NPU WideResNet50.tflite
WideResNet50 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 7.156 ms 1 - 42 MB NPU WideResNet50.dlc
WideResNet50 float SA7255P ADP Qualcomm® SA7255P TFLITE 23.999 ms 0 - 92 MB NPU WideResNet50.tflite
WideResNet50 float SA7255P ADP Qualcomm® SA7255P QNN_DLC 24.375 ms 1 - 41 MB NPU WideResNet50.dlc
WideResNet50 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 4.845 ms 0 - 860 MB NPU WideResNet50.tflite
WideResNet50 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 4.787 ms 1 - 12 MB NPU WideResNet50.dlc
WideResNet50 float SA8295P ADP Qualcomm® SA8295P TFLITE 7.921 ms 0 - 85 MB NPU WideResNet50.tflite
WideResNet50 float SA8295P ADP Qualcomm® SA8295P QNN_DLC 7.886 ms 1 - 33 MB NPU WideResNet50.dlc
WideResNet50 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 4.851 ms 0 - 868 MB NPU WideResNet50.tflite
WideResNet50 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 4.785 ms 1 - 12 MB NPU WideResNet50.dlc
WideResNet50 float SA8775P ADP Qualcomm® SA8775P TFLITE 7.318 ms 0 - 92 MB NPU WideResNet50.tflite
WideResNet50 float SA8775P ADP Qualcomm® SA8775P QNN_DLC 7.156 ms 1 - 42 MB NPU WideResNet50.dlc
WideResNet50 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 3.577 ms 0 - 186 MB NPU WideResNet50.tflite
WideResNet50 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 3.59 ms 1 - 48 MB NPU WideResNet50.dlc
WideResNet50 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 3.576 ms 0 - 49 MB NPU WideResNet50.onnx.zip
WideResNet50 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 3.027 ms 0 - 97 MB NPU WideResNet50.tflite
WideResNet50 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 2.94 ms 1 - 49 MB NPU WideResNet50.dlc
WideResNet50 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 3.03 ms 0 - 43 MB NPU WideResNet50.onnx.zip
WideResNet50 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 2.782 ms 0 - 96 MB NPU WideResNet50.tflite
WideResNet50 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 2.498 ms 1 - 43 MB NPU WideResNet50.dlc
WideResNet50 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 2.817 ms 1 - 50 MB NPU WideResNet50.onnx.zip
WideResNet50 float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 4.653 ms 451 - 451 MB NPU WideResNet50.dlc
WideResNet50 float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 4.473 ms 133 - 133 MB NPU WideResNet50.onnx.zip
WideResNet50 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 3.762 ms 0 - 42 MB NPU WideResNet50.tflite
WideResNet50 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 4.03 ms 0 - 45 MB NPU WideResNet50.dlc
WideResNet50 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 2.076 ms 0 - 108 MB NPU WideResNet50.tflite
WideResNet50 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 2.501 ms 0 - 108 MB NPU WideResNet50.dlc
WideResNet50 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 1.742 ms 0 - 401 MB NPU WideResNet50.tflite
WideResNet50 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 1.873 ms 0 - 365 MB NPU WideResNet50.dlc
WideResNet50 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 2.143 ms 0 - 228 MB NPU WideResNet50.onnx.zip
WideResNet50 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.88 ms 0 - 42 MB NPU WideResNet50.tflite
WideResNet50 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 2.012 ms 0 - 44 MB NPU WideResNet50.dlc
WideResNet50 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 7.377 ms 0 - 97 MB NPU WideResNet50.tflite
WideResNet50 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 9.977 ms 0 - 97 MB NPU WideResNet50.dlc
WideResNet50 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) ONNX 79.946 ms 13 - 29 MB CPU WideResNet50.onnx.zip
WideResNet50 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 24.212 ms 0 - 3 MB NPU WideResNet50.tflite
WideResNet50 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) ONNX 62.122 ms 7 - 86 MB CPU WideResNet50.onnx.zip
WideResNet50 w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 3.762 ms 0 - 42 MB NPU WideResNet50.tflite
WideResNet50 w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 4.03 ms 0 - 45 MB NPU WideResNet50.dlc
WideResNet50 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 1.746 ms 0 - 370 MB NPU WideResNet50.tflite
WideResNet50 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 1.885 ms 0 - 367 MB NPU WideResNet50.dlc
WideResNet50 w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 2.625 ms 0 - 48 MB NPU WideResNet50.tflite
WideResNet50 w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 2.767 ms 0 - 48 MB NPU WideResNet50.dlc
WideResNet50 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 1.739 ms 0 - 392 MB NPU WideResNet50.tflite
WideResNet50 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 1.873 ms 0 - 372 MB NPU WideResNet50.dlc
WideResNet50 w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 1.88 ms 0 - 42 MB NPU WideResNet50.tflite
WideResNet50 w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 2.012 ms 0 - 44 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.314 ms 0 - 106 MB NPU WideResNet50.tflite
WideResNet50 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 1.437 ms 0 - 108 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.577 ms 0 - 114 MB NPU WideResNet50.onnx.zip
WideResNet50 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 1.158 ms 0 - 48 MB NPU WideResNet50.tflite
WideResNet50 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 1.167 ms 0 - 51 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 1.378 ms 0 - 52 MB NPU WideResNet50.onnx.zip
WideResNet50 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile TFLITE 2.814 ms 0 - 92 MB NPU WideResNet50.tflite
WideResNet50 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile QNN_DLC 2.889 ms 0 - 97 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile ONNX 59.512 ms 14 - 30 MB CPU WideResNet50.onnx.zip
WideResNet50 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 1.083 ms 0 - 44 MB NPU WideResNet50.tflite
WideResNet50 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 1.089 ms 0 - 51 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 1.365 ms 0 - 52 MB NPU WideResNet50.onnx.zip
WideResNet50 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 1.809 ms 392 - 392 MB NPU WideResNet50.dlc
WideResNet50 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.848 ms 68 - 68 MB NPU WideResNet50.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.wideresnet50.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.wideresnet50.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.wideresnet50.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.wideresnet50 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.wideresnet50.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.wideresnet50.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 WideResNet50's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of WideResNet50 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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