README.md

---
library_name: pytorch
license: other
tags:
- bu_auto
- 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 Qualcomm Devices

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 is based on the implementation of HRNet-W48-OCR found [here](https://github.com/HRNet/HRNet-Semantic-Segmentation).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/hrnet_w48_ocr) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_w48_ocr/releases/v0.47.0/hrnet_w48_ocr-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_w48_ocr/releases/v0.47.0/hrnet_w48_ocr-onnx-w8a16.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_w48_ocr/releases/v0.47.0/hrnet_w48_ocr-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_w48_ocr/releases/v0.47.0/hrnet_w48_ocr-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[HRNet-W48-OCR on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/hrnet_w48_ocr)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/hrnet_w48_ocr) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [HRNet-W48-OCR on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/hrnet_w48_ocr) for usage instructions.

## 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

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| HRNet-W48-OCR | ONNX | float | Snapdragon® X Elite | 1089.986 ms | 146 - 146 MB | NPU
| HRNet-W48-OCR | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 972.202 ms | 1 - 3922 MB | NPU
| HRNet-W48-OCR | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1204.67 ms | 0 - 168 MB | NPU
| HRNet-W48-OCR | ONNX | float | Qualcomm® QCS9075 | 1389.278 ms | 24 - 51 MB | NPU
| HRNet-W48-OCR | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 784.286 ms | 13 - 2553 MB | NPU
| HRNet-W48-OCR | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 687.218 ms | 35 - 2714 MB | NPU
| HRNet-W48-OCR | ONNX | float | Snapdragon® X2 Elite | 636.212 ms | 148 - 148 MB | NPU

## 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).

## 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).