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HRNet-W48-OCR

Image Segmentation
PyTorch
LiteRT
ONNX
real_time
android
Model card Files Community

HRNet-W48-OCR: Optimized for Mobile Deployment

Semantic segmentation in higher resolution

HRNet-W48-OCR is a machine learning model that can segment images from the Cityscape dataset. It has lightweight and hardware-efficient operations and thus delivers significant speedup on diverse hardware platforms

This model is an implementation of HRNet-W48-OCR found here.

This repository provides scripts to run HRNet-W48-OCR on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.semantic_segmentation
  • Model Stats:
    • Model checkpoint: hrnet_ocr_cs_8162_torch11.pth
    • Input resolution: 2048x1024
    • Number of output classes: 19
    • Number of parameters: 70.3M
    • Model size (float): 268 MB
    • Model size (w8a16): 70.3 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
HRNet-W48-OCR float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 132698.221 ms 11 - 991 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 135327.545 ms 4 - 986 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 74365.943 ms 7 - 1518 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 75510.086 ms 41 - 1131 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 81392.046 ms 4 - 84 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 78352.436 ms 27 - 112 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 76371.06 ms 3 - 983 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 77693.764 ms 1 - 984 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 80866.958 ms 4 - 85 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 77970.107 ms 24 - 114 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 96206.757 ms 2 - 321 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 55819.115 ms 2 - 1110 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 56870.122 ms 30 - 971 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 65554.29 ms 31 - 703 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 43713.273 ms 0 - 959 MB NPU HRNet-W48-OCR.tflite
HRNet-W48-OCR float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 47296.252 ms 24 - 1012 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 50076.088 ms 30 - 755 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 81056.503 ms 324 - 324 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 81404.252 ms 131 - 131 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 12186.267 ms 462 - 1229 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 9037.36 ms 12 - 754 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 8700.597 ms 12 - 131 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 7686.179 ms 5 - 771 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 8647.697 ms 12 - 132 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 8054.785 ms 8 - 256 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 6643.812 ms 12 - 724 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 6265.718 ms 14 - 967 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 10385.889 ms 14 - 868 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 10273.66 ms 14 - 1224 MB NPU HRNet-W48-OCR.onnx
HRNet-W48-OCR w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 8524.584 ms 238 - 238 MB NPU HRNet-W48-OCR.dlc
HRNet-W48-OCR w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 8644.149 ms 88 - 88 MB NPU HRNet-W48-OCR.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[hrnet-w48-ocr]"

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.hrnet_w48_ocr.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.hrnet_w48_ocr.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.hrnet_w48_ocr.export

Profiling Results
------------------------------------------------------------
HRNet-W48-OCR
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : TFLITE                               
Estimated inference time (ms)   : 132698.2                             
Estimated peak memory usage (MB): [11, 991]                            
Total # Ops                     : 579                                  
Compute Unit(s)                 : npu (579 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.hrnet_w48_ocr 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.

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 HRNet-W48-OCR's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of HRNet-W48-OCR can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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