v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- README.md +89 -245
- RTMPose-Body2d_float.dlc +0 -3
- RTMPose-Body2d_float.onnx.zip +0 -3
- RTMPose-Body2d_float.tflite +0 -3
- RTMPose-Body2d_w8a16.dlc +0 -3
- RTMPose-Body2d_w8a16.onnx.zip +0 -3
- tool-versions.yaml +0 -4
README.md
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# RTMPose-Body2d: Optimized for
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## Human pose estimation
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RTMPose is a machine learning model that detects human pose and returns a location and confidence for each of 133 joints.
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This
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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| RTMPose-Body2d |
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Qualcomm®
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Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.
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## Demo off target
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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.rtmpose_body2d.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.rtmpose_body2d.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.rtmpose_body2d.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/rtmpose_body2d/qai_hub_models/models/RTMPose-Body2d/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.rtmpose_body2d 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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device=device,
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)
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```
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Step 3: **Verify on-device accuracy**
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To verify the accuracy of the model on-device, you can run on-device inference
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on sample input data on the same cloud hosted device.
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```python
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input_data = torch_model.sample_inputs()
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inference_job = hub.submit_inference_job(
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model=target_model,
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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.rtmpose_body2d.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.rtmpose_body2d.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 RTMPose-Body2d's performance across various devices [here](https://aihub.qualcomm.com/models/rtmpose_body2d).
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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 RTMPose-Body2d can be found
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[here](https://github.com/open-mmlab/mmpose/blob/main/LICENSE).
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## References
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* [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://arxiv.org/abs/2303.07399)
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* [Source Model Implementation](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose)
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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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# RTMPose-Body2d: Optimized for Qualcomm Devices
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RTMPose is a machine learning model that detects human pose and returns a location and confidence for each of 133 joints.
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This is based on the implementation of RTMPose-Body2d found [here](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose).
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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/rtmpose_body2d) 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/rtmpose_body2d/releases/v0.46.1/rtmpose_body2d-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/rtmpose_body2d/releases/v0.46.1/rtmpose_body2d-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/rtmpose_body2d/releases/v0.46.1/rtmpose_body2d-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/rtmpose_body2d/releases/v0.46.1/rtmpose_body2d-qnn_dlc-w8a16.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/rtmpose_body2d/releases/v0.46.1/rtmpose_body2d-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[RTMPose-Body2d on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/rtmpose_body2d)**.
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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/rtmpose_body2d) 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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See our repository for [RTMPose-Body2d on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/rtmpose_body2d) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.pose_estimation
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**Model Stats:**
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- Input resolution: 256x192
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- Number of parameters: 17.9M
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- Model size (float): 68.5 MB
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- Model size (w8a16): 18.2 MB
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| RTMPose-Body2d | ONNX | float | Snapdragon® X Elite | 2.288 ms | 36 - 36 MB | NPU
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| RTMPose-Body2d | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1.726 ms | 0 - 120 MB | NPU
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| RTMPose-Body2d | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2.349 ms | 0 - 40 MB | NPU
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| RTMPose-Body2d | ONNX | float | Qualcomm® QCS9075 | 3.003 ms | 1 - 5 MB | NPU
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| RTMPose-Body2d | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.425 ms | 0 - 101 MB | NPU
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| RTMPose-Body2d | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.264 ms | 0 - 100 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Snapdragon® X Elite | 2.718 ms | 19 - 19 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.824 ms | 0 - 150 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Qualcomm® QCS6490 | 182.531 ms | 48 - 60 MB | CPU
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| RTMPose-Body2d | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 2.477 ms | 0 - 6 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Qualcomm® QCS9075 | 2.829 ms | 0 - 3 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Qualcomm® QCM6690 | 88.493 ms | 47 - 55 MB | CPU
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| RTMPose-Body2d | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 1.395 ms | 0 - 118 MB | NPU
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| RTMPose-Body2d | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 84.503 ms | 47 - 56 MB | CPU
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| RTMPose-Body2d | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 1.259 ms | 0 - 122 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Snapdragon® X Elite | 1.853 ms | 1 - 1 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.334 ms | 0 - 53 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 1.737 ms | 1 - 2 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Qualcomm® SA8775P | 2.438 ms | 1 - 35 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Qualcomm® QCS9075 | 2.396 ms | 1 - 3 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 3.545 ms | 0 - 62 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Qualcomm® SA8295P | 3.571 ms | 0 - 35 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.09 ms | 0 - 34 MB | NPU
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| RTMPose-Body2d | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.916 ms | 1 - 36 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Snapdragon® X Elite | 1.944 ms | 0 - 0 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 1.205 ms | 0 - 75 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 1.73 ms | 0 - 2 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Qualcomm® SA8775P | 2.044 ms | 0 - 52 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 1.878 ms | 0 - 2 MB | NPU
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| RTMPose-Body2d | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.883 ms | 0 - 49 MB | NPU
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+
| RTMPose-Body2d | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.748 ms | 0 - 50 MB | NPU
|
| 94 |
+
| RTMPose-Body2d | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1.306 ms | 0 - 92 MB | NPU
|
| 95 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 7.561 ms | 0 - 50 MB | NPU
|
| 96 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 1.707 ms | 0 - 2 MB | NPU
|
| 97 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® SA8775P | 2.484 ms | 0 - 51 MB | NPU
|
| 98 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® QCS9075 | 2.426 ms | 0 - 40 MB | NPU
|
| 99 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 3.523 ms | 0 - 94 MB | NPU
|
| 100 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® SA7255P | 7.561 ms | 0 - 50 MB | NPU
|
| 101 |
+
| RTMPose-Body2d | TFLITE | float | Qualcomm® SA8295P | 3.55 ms | 0 - 57 MB | NPU
|
| 102 |
+
| RTMPose-Body2d | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.087 ms | 0 - 47 MB | NPU
|
| 103 |
+
| RTMPose-Body2d | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.908 ms | 0 - 50 MB | NPU
|
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| 104 |
|
| 105 |
## License
|
| 106 |
* The license for the original implementation of RTMPose-Body2d can be found
|
| 107 |
[here](https://github.com/open-mmlab/mmpose/blob/main/LICENSE).
|
| 108 |
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| 109 |
## References
|
| 110 |
* [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://arxiv.org/abs/2303.07399)
|
| 111 |
* [Source Model Implementation](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose)
|
| 112 |
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| 113 |
## Community
|
| 114 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 115 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
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RTMPose-Body2d_float.dlc
DELETED
|
@@ -1,3 +0,0 @@
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| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:2150e5fa5ff4e552f141ac50e63a47040cf6ce242964ba8f113fe3325f5b35fe
|
| 3 |
-
size 72018500
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RTMPose-Body2d_float.onnx.zip
DELETED
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|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:f910029d75e11657b05e9255bc31c67767c95315fbf8b0f99aa8651d1083c281
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| 3 |
-
size 67069003
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RTMPose-Body2d_float.tflite
DELETED
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|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:1c47529a0df65a1913fcc19b8aa6a345cc987f24336f28477f7c37bd3db701e5
|
| 3 |
-
size 71821120
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RTMPose-Body2d_w8a16.dlc
DELETED
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@@ -1,3 +0,0 @@
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|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:7e01d928bf68974c92ec325ac6ec4b569e05a3d1352c9400352c68ea19e7249a
|
| 3 |
-
size 19063844
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RTMPose-Body2d_w8a16.onnx.zip
DELETED
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@@ -1,3 +0,0 @@
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|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:64d138427232b5d78d781152492db08af621bf5d2bc727ee02d935567d1f2ef6
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| 3 |
-
size 31788064
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tool-versions.yaml
DELETED
|
@@ -1,4 +0,0 @@
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|
| 1 |
-
tool_versions:
|
| 2 |
-
onnx:
|
| 3 |
-
qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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