v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- HRNetPose_float.dlc +0 -3
- HRNetPose_float.onnx.zip +0 -3
- HRNetPose_float.tflite +0 -3
- HRNetPose_w8a16.dlc +0 -3
- HRNetPose_w8a16.onnx.zip +0 -3
- HRNetPose_w8a8.dlc +0 -3
- HRNetPose_w8a8.tflite +0 -3
- README.md +128 -278
- tool-versions.yaml +0 -3
HRNetPose_float.dlc
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README.md
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# HRNetPose: Optimized for
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## Perform accurate human pose estimation
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HRNet performs pose estimation in high-resolution representations.
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The package contains a simple end-to-end demo that downloads pre-trained
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weights and runs this model on a sample input.
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```bash
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python -m qai_hub_models.models.hrnet_pose.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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inference, and post processing.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.hrnet_pose.demo
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```
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### Run model on a cloud-hosted device
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Downloads compiled assets that can be deployed on-device for Android.
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* Accuracy check between PyTorch and on-device outputs.
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```bash
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python -m qai_hub_models.models.hrnet_pose.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/hrnet_pose/qai_hub_models/models/HRNetPose/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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To compile a PyTorch model for on-device deployment, we first trace the model
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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```python
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import torch
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import qai_hub as hub
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from qai_hub_models.models.hrnet_pose import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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# Trace model
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input_shape = torch_model.get_input_spec()
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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model=pt_model,
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device=device,
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input_specs=torch_model.get_input_spec(),
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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Step 2: **Performance profiling on cloud-hosted device**
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After compiling models from step 1. Models can be profiled model on-device using the
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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```python
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profile_job = hub.submit_profile_job(
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model=target_model,
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)
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```
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Step 3: **Verify on-device accuracy**
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on sample input data on the same cloud hosted device.
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```python
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inference_job = hub.submit_inference_job(
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device=device,
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inputs=input_data,
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)
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on_device_output = inference_job.download_output_data()
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```
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With the output of the model, you can compute like PSNR, relative errors or
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spot check the output with expected output.
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**Note**: This on-device profiling and inference requires access to Qualcomm®
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Run demo on a cloud-hosted device
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You can also run the demo on-device.
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```bash
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python -m qai_hub_models.models.hrnet_pose.demo --eval-mode on-device
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```
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.hrnet_pose.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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- TensorFlow Lite (`.tflite` export): [This
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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guide to deploy the .tflite model in an Android application.
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- QNN (`.so` export ): This [sample
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on HRNetPose's performance across various devices [here](https://aihub.qualcomm.com/models/hrnet_pose).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of HRNetPose can be found
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[here](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch/blob/master/LICENSE).
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## References
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* [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212)
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* [Source Model Implementation](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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# HRNetPose: Optimized for Qualcomm Devices
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HRNet performs pose estimation in high-resolution representations.
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This is based on the implementation of HRNetPose found [here](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
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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_pose) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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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.
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## Getting Started
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There are two ways to deploy this model on your device:
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### Option 1: Download Pre-Exported Models
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Below are pre-exported model assets ready for deployment.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-onnx-float.zip)
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| ONNX | w8a16 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-onnx-w8a16.zip)
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-qnn_dlc-float.zip)
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| QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-qnn_dlc-w8a16.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-qnn_dlc-w8a8.zip)
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| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/hrnet_pose/releases/v0.46.1/hrnet_pose-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[HRNetPose on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/hrnet_pose)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/hrnet_pose) Python library to compile and export the model with your own:
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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| 51 |
+
See our repository for [HRNetPose on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/hrnet_pose) for usage instructions.
|
| 52 |
+
|
| 53 |
+
## Model Details
|
| 54 |
+
|
| 55 |
+
**Model Type:** Model_use_case.pose_estimation
|
| 56 |
+
|
| 57 |
+
**Model Stats:**
|
| 58 |
+
- Model checkpoint: hrnet_posenet_FP32_state_dict
|
| 59 |
+
- Input resolution: 256x192
|
| 60 |
+
- Number of parameters: 28.5M
|
| 61 |
+
- Model size (float): 109 MB
|
| 62 |
+
- Model size (w8a8): 28.1 MB
|
| 63 |
+
|
| 64 |
+
## Performance Summary
|
| 65 |
+
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|
| 66 |
+
|---|---|---|---|---|---|---
|
| 67 |
+
| HRNetPose | ONNX | float | Snapdragon® X Elite | 2.663 ms | 55 - 55 MB | NPU
|
| 68 |
+
| HRNetPose | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 2.073 ms | 0 - 197 MB | NPU
|
| 69 |
+
| HRNetPose | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2.802 ms | 1 - 3 MB | NPU
|
| 70 |
+
| HRNetPose | ONNX | float | Qualcomm® QCS9075 | 4.107 ms | 0 - 4 MB | NPU
|
| 71 |
+
| HRNetPose | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.748 ms | 0 - 142 MB | NPU
|
| 72 |
+
| HRNetPose | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.423 ms | 0 - 143 MB | NPU
|
| 73 |
+
| HRNetPose | ONNX | w8a16 | Snapdragon® X Elite | 1.978 ms | 28 - 28 MB | NPU
|
| 74 |
+
| HRNetPose | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.511 ms | 0 - 258 MB | NPU
|
| 75 |
+
| HRNetPose | ONNX | w8a16 | Qualcomm® QCS6490 | 486.81 ms | 32 - 41 MB | CPU
|
| 76 |
+
| HRNetPose | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 2.014 ms | 0 - 268 MB | NPU
|
| 77 |
+
| HRNetPose | ONNX | w8a16 | Qualcomm® QCS9075 | 2.297 ms | 0 - 3 MB | NPU
|
| 78 |
+
| HRNetPose | ONNX | w8a16 | Qualcomm® QCM6690 | 214.195 ms | 30 - 48 MB | CPU
|
| 79 |
+
| HRNetPose | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.232 ms | 0 - 188 MB | NPU
|
| 80 |
+
| HRNetPose | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 209.371 ms | 26 - 39 MB | CPU
|
| 81 |
+
| HRNetPose | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.996 ms | 0 - 189 MB | NPU
|
| 82 |
+
| HRNetPose | QNN_DLC | float | Snapdragon® X Elite | 2.876 ms | 1 - 1 MB | NPU
|
| 83 |
+
| HRNetPose | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 2.006 ms | 0 - 121 MB | NPU
|
| 84 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 14.33 ms | 1 - 75 MB | NPU
|
| 85 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.677 ms | 1 - 87 MB | NPU
|
| 86 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® SA8775P | 18.856 ms | 1 - 75 MB | NPU
|
| 87 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® QCS9075 | 4.118 ms | 1 - 3 MB | NPU
|
| 88 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 4.961 ms | 0 - 105 MB | NPU
|
| 89 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® SA7255P | 14.33 ms | 1 - 75 MB | NPU
|
| 90 |
+
| HRNetPose | QNN_DLC | float | Qualcomm® SA8295P | 4.517 ms | 0 - 67 MB | NPU
|
| 91 |
+
| HRNetPose | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.586 ms | 1 - 77 MB | NPU
|
| 92 |
+
| HRNetPose | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.267 ms | 0 - 78 MB | NPU
|
| 93 |
+
| HRNetPose | QNN_DLC | w8a16 | Snapdragon® X Elite | 2.162 ms | 0 - 0 MB | NPU
|
| 94 |
+
| HRNetPose | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.394 ms | 0 - 151 MB | NPU
|
| 95 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 6.753 ms | 0 - 2 MB | NPU
|
| 96 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 5.247 ms | 0 - 98 MB | NPU
|
| 97 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 1.918 ms | 0 - 2 MB | NPU
|
| 98 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® SA8775P | 2.261 ms | 0 - 100 MB | NPU
|
| 99 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 2.196 ms | 2 - 4 MB | NPU
|
| 100 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 21.984 ms | 0 - 217 MB | NPU
|
| 101 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 2.609 ms | 0 - 152 MB | NPU
|
| 102 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® SA7255P | 5.247 ms | 0 - 98 MB | NPU
|
| 103 |
+
| HRNetPose | QNN_DLC | w8a16 | Qualcomm® SA8295P | 3.086 ms | 0 - 96 MB | NPU
|
| 104 |
+
| HRNetPose | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.018 ms | 0 - 99 MB | NPU
|
| 105 |
+
| HRNetPose | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 2.557 ms | 0 - 101 MB | NPU
|
| 106 |
+
| HRNetPose | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.812 ms | 0 - 100 MB | NPU
|
| 107 |
+
| HRNetPose | QNN_DLC | w8a8 | Snapdragon® X Elite | 1.269 ms | 0 - 0 MB | NPU
|
| 108 |
+
| HRNetPose | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.829 ms | 0 - 133 MB | NPU
|
| 109 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 3.664 ms | 2 - 4 MB | NPU
|
| 110 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 2.869 ms | 0 - 88 MB | NPU
|
| 111 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 1.124 ms | 0 - 4 MB | NPU
|
| 112 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® SA8775P | 5.323 ms | 0 - 88 MB | NPU
|
| 113 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 1.309 ms | 0 - 2 MB | NPU
|
| 114 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 10.276 ms | 0 - 209 MB | NPU
|
| 115 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.714 ms | 0 - 135 MB | NPU
|
| 116 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® SA7255P | 2.869 ms | 0 - 88 MB | NPU
|
| 117 |
+
| HRNetPose | QNN_DLC | w8a8 | Qualcomm® SA8295P | 1.893 ms | 0 - 87 MB | NPU
|
| 118 |
+
| HRNetPose | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.642 ms | 0 - 92 MB | NPU
|
| 119 |
+
| HRNetPose | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.507 ms | 0 - 88 MB | NPU
|
| 120 |
+
| HRNetPose | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.564 ms | 0 - 92 MB | NPU
|
| 121 |
+
| HRNetPose | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.028 ms | 0 - 195 MB | NPU
|
| 122 |
+
| HRNetPose | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 14.366 ms | 0 - 120 MB | NPU
|
| 123 |
+
| HRNetPose | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.675 ms | 0 - 3 MB | NPU
|
| 124 |
+
| HRNetPose | TFLITE | float | Qualcomm® SA8775P | 4.37 ms | 0 - 119 MB | NPU
|
| 125 |
+
| HRNetPose | TFLITE | float | Qualcomm® QCS9075 | 4.13 ms | 0 - 58 MB | NPU
|
| 126 |
+
| HRNetPose | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 4.964 ms | 0 - 180 MB | NPU
|
| 127 |
+
| HRNetPose | TFLITE | float | Qualcomm® SA7255P | 14.366 ms | 0 - 120 MB | NPU
|
| 128 |
+
| HRNetPose | TFLITE | float | Qualcomm® SA8295P | 4.555 ms | 0 - 107 MB | NPU
|
| 129 |
+
| HRNetPose | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.583 ms | 0 - 120 MB | NPU
|
| 130 |
+
| HRNetPose | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.266 ms | 0 - 121 MB | NPU
|
| 131 |
+
| HRNetPose | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.701 ms | 0 - 143 MB | NPU
|
| 132 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCS6490 | 3.334 ms | 0 - 30 MB | NPU
|
| 133 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 2.605 ms | 0 - 88 MB | NPU
|
| 134 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.966 ms | 0 - 3 MB | NPU
|
| 135 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® SA8775P | 1.301 ms | 0 - 92 MB | NPU
|
| 136 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCS9075 | 1.097 ms | 0 - 30 MB | NPU
|
| 137 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCM6690 | 9.757 ms | 0 - 200 MB | NPU
|
| 138 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.485 ms | 0 - 145 MB | NPU
|
| 139 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® SA7255P | 2.605 ms | 0 - 88 MB | NPU
|
| 140 |
+
| HRNetPose | TFLITE | w8a8 | Qualcomm® SA8295P | 1.723 ms | 0 - 85 MB | NPU
|
| 141 |
+
| HRNetPose | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.583 ms | 0 - 91 MB | NPU
|
| 142 |
+
| HRNetPose | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.341 ms | 0 - 86 MB | NPU
|
| 143 |
+
| HRNetPose | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.518 ms | 0 - 92 MB | NPU
|
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|
| 144 |
|
| 145 |
## License
|
| 146 |
* The license for the original implementation of HRNetPose can be found
|
| 147 |
[here](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch/blob/master/LICENSE).
|
| 148 |
|
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|
| 149 |
## References
|
| 150 |
* [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212)
|
| 151 |
* [Source Model Implementation](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)
|
| 152 |
|
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|
| 153 |
## Community
|
| 154 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 155 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
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|
tool-versions.yaml
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
tool_versions:
|
| 2 |
-
qnn_dlc:
|
| 3 |
-
qairt: 2.41.0.251128145156_191518
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