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README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- real_time
+- android
+pipeline_tag: image-segmentation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_w48_ocr/web-assets/model_demo.png)
+
+# 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](https://github.com/HRNet/HRNet-Semantic-Segmentation).
+
+
+This repository provides scripts to run HRNet-W48-OCR on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/hrnet_w48_ocr).
+
+
+### 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: 268 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 | 136903.912 ms | 5 - 603 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 137707.961 ms | 24 - 33 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 78009.618 ms | 13 - 905 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN | 83347.995 ms | 39 - 665 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 79770.495 ms | 6 - 92 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 89415.101 ms | 25 - 27 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 78803.442 ms | 5 - 604 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN | 80186.288 ms | 24 - 34 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 82827.147 ms | 4 - 91 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 88501.039 ms | 24 - 112 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 88885.691 ms | 6 - 351 MB | NPU | [HRNet-W48-OCR.onnx](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.onnx) |
+| HRNet-W48-OCR | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 57260.411 ms | 7 - 534 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 57891.95 ms | 31 - 438 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 64628.532 ms | 25 - 448 MB | NPU | [HRNet-W48-OCR.onnx](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.onnx) |
+| HRNet-W48-OCR | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 38438.694 ms | 0 - 598 MB | NPU | [HRNet-W48-OCR.tflite](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.tflite) |
+| HRNet-W48-OCR | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 44135.938 ms | 24 - 515 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 55970.899 ms | 26 - 513 MB | NPU | [HRNet-W48-OCR.onnx](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.onnx) |
+| HRNet-W48-OCR | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 84088.666 ms | 24 - 24 MB | NPU | Use Export Script |
+| HRNet-W48-OCR | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 81968.541 ms | 131 - 131 MB | NPU | [HRNet-W48-OCR.onnx](https://huggingface.co/qualcomm/HRNet-W48-OCR/blob/main/HRNet-W48-OCR.onnx) |
+
+
+
+
+## Installation
+
+
+Install the package via pip:
+```bash
+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](https://app.aihub.qualcomm.com/) 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.
+```bash
+qai-hub configure --api_token API_TOKEN
+```
+Navigate to [docs](https://app.aihub.qualcomm.com/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.
+
+```bash
+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.
+
+```bash
+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)   : 136903.9                             
+Estimated peak memory usage (MB): [5, 603]                             
+Total # Ops                     : 578                                  
+Compute Unit(s)                 : npu (578 ops) gpu (0 ops) cpu (0 ops)
+```
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/hrnet_w48_ocr/qai_hub_models/models/HRNet-W48-OCR/export.py)
+leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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.
+
+```python
+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.
+```python
+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.
+```python
+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](https://myaccount.qualcomm.com/signup).
+
+
+
+
+## Deploying compiled model to Android
+
+
+The models can be deployed using multiple runtimes:
+- TensorFlow Lite (`.tflite` export): [This
+  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
+  guide to deploy the .tflite model in an Android application.
+
+
+- QNN (`.so` export ): This [sample
+  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
+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](https://aihub.qualcomm.com/models/hrnet_w48_ocr).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of HRNet-W48-OCR can be found
+  [here](https://github.com/HRNet/HRNet-Semantic-Segmentation/blob/HRNet-OCR/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
+
+
+
+## References
+* [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/abs/1909.11065)
+* [Source Model Implementation](https://github.com/HRNet/HRNet-Semantic-Segmentation)
+
+
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
+
+