v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- MediaPipe-Pose-Estimation_PoseDetector_float.dlc +0 -3
- MediaPipe-Pose-Estimation_PoseDetector_float.onnx.zip +0 -3
- MediaPipe-Pose-Estimation_PoseDetector_float.tflite +0 -3
- MediaPipe-Pose-Estimation_PoseDetector_w8a8.dlc +0 -3
- MediaPipe-Pose-Estimation_PoseDetector_w8a8.onnx.zip +0 -3
- MediaPipe-Pose-Estimation_PoseDetector_w8a8.tflite +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.dlc +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.onnx.zip +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.tflite +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_w8a8.dlc +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_w8a8.onnx.zip +0 -3
- MediaPipe-Pose-Estimation_PoseLandmarkDetector_w8a8.tflite +0 -3
- README.md +176 -309
- tool-versions.yaml +0 -4
MediaPipe-Pose-Estimation_PoseDetector_float.dlc
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MediaPipe-Pose-Estimation_PoseDetector_float.onnx.zip
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MediaPipe-Pose-Estimation_PoseDetector_float.tflite
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MediaPipe-Pose-Estimation_PoseDetector_w8a8.dlc
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MediaPipe-Pose-Estimation_PoseDetector_w8a8.onnx.zip
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MediaPipe-Pose-Estimation_PoseDetector_w8a8.tflite
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MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.dlc
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MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.onnx.zip
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MediaPipe-Pose-Estimation_PoseLandmarkDetector_float.tflite
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MediaPipe-Pose-Estimation_PoseLandmarkDetector_w8a8.dlc
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MediaPipe-Pose-Estimation_PoseLandmarkDetector_w8a8.tflite
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README.md
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# MediaPipe-Pose-Estimation: Optimized for
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## Detect and track human face, hand, and torso in real-time images and video streams
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The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of the face, hands, and torso in an image.
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Qualcomm®
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```bash
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python -m qai_hub_models.models.mediapipe_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.mediapipe_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.mediapipe_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/mediapipe_pose/qai_hub_models/models/MediaPipe-Pose-Estimation/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.mediapipe_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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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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## 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 MediaPipe-Pose-Estimation's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_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 MediaPipe-Pose-Estimation can be found
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[here](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE).
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## References
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* [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/abs/2006.10204)
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* [Source Model Implementation](https://github.com/zmurez/MediaPipePyTorch/)
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| 329 |
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| 330 |
-
|
| 331 |
-
|
| 332 |
## Community
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| 333 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 334 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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| 335 |
-
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| 336 |
-
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| 10 |
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| 11 |
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| 12 |
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| 13 |
+
# MediaPipe-Pose-Estimation: Optimized for Qualcomm Devices
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|
| 14 |
|
| 15 |
The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of the face, hands, and torso in an image.
|
| 16 |
|
| 17 |
+
This is based on the implementation of MediaPipe-Pose-Estimation found [here](https://github.com/zmurez/MediaPipePyTorch/).
|
| 18 |
+
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/mediapipe_pose) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
|
| 19 |
+
|
| 20 |
+
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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| 21 |
+
|
| 22 |
+
## Getting Started
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| 23 |
+
There are two ways to deploy this model on your device:
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| 24 |
+
|
| 25 |
+
### Option 1: Download Pre-Exported Models
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| 26 |
+
|
| 27 |
+
Below are pre-exported model assets ready for deployment.
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| 28 |
+
|
| 29 |
+
| Runtime | Precision | Chipset | SDK Versions | Download |
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| 30 |
+
|---|---|---|---|---|
|
| 31 |
+
| 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/mediapipe_pose/releases/v0.46.1/mediapipe_pose-onnx-float.zip)
|
| 32 |
+
| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_pose/releases/v0.46.1/mediapipe_pose-onnx-w8a8.zip)
|
| 33 |
+
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_pose/releases/v0.46.1/mediapipe_pose-qnn_dlc-float.zip)
|
| 34 |
+
| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_pose/releases/v0.46.1/mediapipe_pose-qnn_dlc-w8a8.zip)
|
| 35 |
+
| 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/mediapipe_pose/releases/v0.46.1/mediapipe_pose-tflite-float.zip)
|
| 36 |
+
| 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/mediapipe_pose/releases/v0.46.1/mediapipe_pose-tflite-w8a8.zip)
|
| 37 |
+
|
| 38 |
+
For more device-specific assets and performance metrics, visit **[MediaPipe-Pose-Estimation on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/mediapipe_pose)**.
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
### Option 2: Export with Custom Configurations
|
| 42 |
+
|
| 43 |
+
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mediapipe_pose) Python library to compile and export the model with your own:
|
| 44 |
+
- Custom weights (e.g., fine-tuned checkpoints)
|
| 45 |
+
- Custom input shapes
|
| 46 |
+
- Target device and runtime configurations
|
| 47 |
+
|
| 48 |
+
This option is ideal if you need to customize the model beyond the default configuration provided here.
|
| 49 |
+
|
| 50 |
+
See our repository for [MediaPipe-Pose-Estimation on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mediapipe_pose) for usage instructions.
|
| 51 |
+
|
| 52 |
+
## Model Details
|
| 53 |
+
|
| 54 |
+
**Model Type:** Model_use_case.pose_estimation
|
| 55 |
+
|
| 56 |
+
**Model Stats:**
|
| 57 |
+
- Input resolution: 256x256
|
| 58 |
+
- Number of parameters (PoseDetector): 815K
|
| 59 |
+
- Model size (PoseDetector) (float): 3.14 MB
|
| 60 |
+
- Number of parameters (PoseLandmarkDetector): 3.36M
|
| 61 |
+
- Model size (PoseLandmarkDetector) (float): 12.9 MB
|
| 62 |
+
|
| 63 |
+
## Performance Summary
|
| 64 |
+
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|
| 65 |
+
|---|---|---|---|---|---|---
|
| 66 |
+
| PoseDetector | ONNX | float | Snapdragon® X Elite | 1.041 ms | 2 - 2 MB | NPU
|
| 67 |
+
| PoseDetector | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.845 ms | 0 - 120 MB | NPU
|
| 68 |
+
| PoseDetector | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1.101 ms | 0 - 122 MB | NPU
|
| 69 |
+
| PoseDetector | ONNX | float | Qualcomm® QCS9075 | 1.786 ms | 0 - 3 MB | NPU
|
| 70 |
+
| PoseDetector | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.664 ms | 0 - 101 MB | NPU
|
| 71 |
+
| PoseDetector | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.548 ms | 0 - 102 MB | NPU
|
| 72 |
+
| PoseDetector | ONNX | w8a8 | Snapdragon® X Elite | 0.439 ms | 0 - 0 MB | NPU
|
| 73 |
+
| PoseDetector | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.429 ms | 0 - 114 MB | NPU
|
| 74 |
+
| PoseDetector | ONNX | w8a8 | Qualcomm® QCS6490 | 13.894 ms | 9 - 14 MB | CPU
|
| 75 |
+
| PoseDetector | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.543 ms | 0 - 109 MB | NPU
|
| 76 |
+
| PoseDetector | ONNX | w8a8 | Qualcomm® QCS9075 | 0.643 ms | 0 - 3 MB | NPU
|
| 77 |
+
| PoseDetector | ONNX | w8a8 | Qualcomm® QCM6690 | 10.21 ms | 10 - 19 MB | CPU
|
| 78 |
+
| PoseDetector | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.304 ms | 0 - 99 MB | NPU
|
| 79 |
+
| PoseDetector | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 7.585 ms | 9 - 19 MB | CPU
|
| 80 |
+
| PoseDetector | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.286 ms | 0 - 103 MB | NPU
|
| 81 |
+
| PoseDetector | QNN_DLC | float | Snapdragon® X Elite | 1.021 ms | 0 - 0 MB | NPU
|
| 82 |
+
| PoseDetector | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.625 ms | 0 - 50 MB | NPU
|
| 83 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 5.758 ms | 0 - 32 MB | NPU
|
| 84 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.851 ms | 0 - 2 MB | NPU
|
| 85 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® SA8775P | 1.59 ms | 0 - 33 MB | NPU
|
| 86 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® QCS9075 | 1.497 ms | 0 - 2 MB | NPU
|
| 87 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 2.173 ms | 0 - 43 MB | NPU
|
| 88 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® SA7255P | 5.758 ms | 0 - 32 MB | NPU
|
| 89 |
+
| PoseDetector | QNN_DLC | float | Qualcomm® SA8295P | 2.546 ms | 0 - 23 MB | NPU
|
| 90 |
+
| PoseDetector | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.517 ms | 0 - 35 MB | NPU
|
| 91 |
+
| PoseDetector | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.397 ms | 0 - 34 MB | NPU
|
| 92 |
+
| PoseDetector | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.434 ms | 0 - 0 MB | NPU
|
| 93 |
+
| PoseDetector | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.218 ms | 0 - 41 MB | NPU
|
| 94 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.518 ms | 0 - 2 MB | NPU
|
| 95 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 0.986 ms | 0 - 29 MB | NPU
|
| 96 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.318 ms | 0 - 1 MB | NPU
|
| 97 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.5 ms | 0 - 31 MB | NPU
|
| 98 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.402 ms | 0 - 2 MB | NPU
|
| 99 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 3.206 ms | 0 - 142 MB | NPU
|
| 100 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.572 ms | 0 - 43 MB | NPU
|
| 101 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® SA7255P | 0.986 ms | 0 - 29 MB | NPU
|
| 102 |
+
| PoseDetector | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.91 ms | 0 - 26 MB | NPU
|
| 103 |
+
| PoseDetector | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.188 ms | 0 - 31 MB | NPU
|
| 104 |
+
| PoseDetector | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.408 ms | 0 - 26 MB | NPU
|
| 105 |
+
| PoseDetector | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.155 ms | 0 - 31 MB | NPU
|
| 106 |
+
| PoseDetector | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.627 ms | 0 - 50 MB | NPU
|
| 107 |
+
| PoseDetector | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 5.861 ms | 0 - 32 MB | NPU
|
| 108 |
+
| PoseDetector | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.851 ms | 0 - 2 MB | NPU
|
| 109 |
+
| PoseDetector | TFLITE | float | Qualcomm® SA8775P | 1.598 ms | 0 - 37 MB | NPU
|
| 110 |
+
| PoseDetector | TFLITE | float | Qualcomm® QCS9075 | 1.493 ms | 0 - 5 MB | NPU
|
| 111 |
+
| PoseDetector | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 2.168 ms | 0 - 43 MB | NPU
|
| 112 |
+
| PoseDetector | TFLITE | float | Qualcomm® SA7255P | 5.861 ms | 0 - 32 MB | NPU
|
| 113 |
+
| PoseDetector | TFLITE | float | Qualcomm® SA8295P | 2.549 ms | 0 - 25 MB | NPU
|
| 114 |
+
| PoseDetector | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.517 ms | 0 - 30 MB | NPU
|
| 115 |
+
| PoseDetector | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.398 ms | 0 - 36 MB | NPU
|
| 116 |
+
| PoseDetector | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.224 ms | 0 - 39 MB | NPU
|
| 117 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCS6490 | 1.56 ms | 0 - 5 MB | NPU
|
| 118 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.037 ms | 0 - 29 MB | NPU
|
| 119 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.321 ms | 0 - 75 MB | NPU
|
| 120 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® SA8775P | 1.9 ms | 0 - 28 MB | NPU
|
| 121 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.42 ms | 0 - 3 MB | NPU
|
| 122 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCM6690 | 3.201 ms | 0 - 141 MB | NPU
|
| 123 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.573 ms | 0 - 41 MB | NPU
|
| 124 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® SA7255P | 1.037 ms | 0 - 29 MB | NPU
|
| 125 |
+
| PoseDetector | TFLITE | w8a8 | Qualcomm® SA8295P | 0.94 ms | 0 - 25 MB | NPU
|
| 126 |
+
| PoseDetector | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.192 ms | 0 - 25 MB | NPU
|
| 127 |
+
| PoseDetector | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.391 ms | 0 - 25 MB | NPU
|
| 128 |
+
| PoseDetector | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.161 ms | 0 - 31 MB | NPU
|
| 129 |
+
| PoseLandmarkDetector | ONNX | float | Snapdragon® X Elite | 1.16 ms | 7 - 7 MB | NPU
|
| 130 |
+
| PoseLandmarkDetector | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.882 ms | 0 - 145 MB | NPU
|
| 131 |
+
| PoseLandmarkDetector | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1.167 ms | 0 - 9 MB | NPU
|
| 132 |
+
| PoseLandmarkDetector | ONNX | float | Qualcomm® QCS9075 | 1.667 ms | 1 - 3 MB | NPU
|
| 133 |
+
| PoseLandmarkDetector | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.757 ms | 0 - 110 MB | NPU
|
| 134 |
+
| PoseLandmarkDetector | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.636 ms | 0 - 112 MB | NPU
|
| 135 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Snapdragon® X Elite | 0.475 ms | 4 - 4 MB | NPU
|
| 136 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.367 ms | 0 - 135 MB | NPU
|
| 137 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Qualcomm® QCS6490 | 19.015 ms | 7 - 14 MB | CPU
|
| 138 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.535 ms | 0 - 123 MB | NPU
|
| 139 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Qualcomm® QCS9075 | 0.725 ms | 0 - 3 MB | NPU
|
| 140 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Qualcomm® QCM6690 | 9.154 ms | 9 - 20 MB | CPU
|
| 141 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.326 ms | 0 - 119 MB | NPU
|
| 142 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 6.872 ms | 8 - 20 MB | CPU
|
| 143 |
+
| PoseLandmarkDetector | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.308 ms | 0 - 120 MB | NPU
|
| 144 |
+
| PoseLandmarkDetector | QNN_DLC | float | Snapdragon® X Elite | 1.082 ms | 1 - 1 MB | NPU
|
| 145 |
+
| PoseLandmarkDetector | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.603 ms | 0 - 76 MB | NPU
|
| 146 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 3.175 ms | 0 - 41 MB | NPU
|
| 147 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.802 ms | 1 - 2 MB | NPU
|
| 148 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® SA8775P | 1.387 ms | 1 - 44 MB | NPU
|
| 149 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® QCS9075 | 1.229 ms | 1 - 3 MB | NPU
|
| 150 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.138 ms | 0 - 68 MB | NPU
|
| 151 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® SA7255P | 3.175 ms | 0 - 41 MB | NPU
|
| 152 |
+
| PoseLandmarkDetector | QNN_DLC | float | Qualcomm® SA8295P | 1.432 ms | 0 - 32 MB | NPU
|
| 153 |
+
| PoseLandmarkDetector | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.498 ms | 1 - 41 MB | NPU
|
| 154 |
+
| PoseLandmarkDetector | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.421 ms | 1 - 45 MB | NPU
|
| 155 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.435 ms | 0 - 0 MB | NPU
|
| 156 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.204 ms | 0 - 54 MB | NPU
|
| 157 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.078 ms | 0 - 2 MB | NPU
|
| 158 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 0.795 ms | 0 - 39 MB | NPU
|
| 159 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.3 ms | 0 - 2 MB | NPU
|
| 160 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.503 ms | 0 - 43 MB | NPU
|
| 161 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.444 ms | 0 - 2 MB | NPU
|
| 162 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.584 ms | 0 - 38 MB | NPU
|
| 163 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.458 ms | 0 - 56 MB | NPU
|
| 164 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® SA7255P | 0.795 ms | 0 - 39 MB | NPU
|
| 165 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.743 ms | 0 - 38 MB | NPU
|
| 166 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.195 ms | 0 - 43 MB | NPU
|
| 167 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.314 ms | 0 - 38 MB | NPU
|
| 168 |
+
| PoseLandmarkDetector | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.163 ms | 0 - 43 MB | NPU
|
| 169 |
+
| PoseLandmarkDetector | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.597 ms | 0 - 79 MB | NPU
|
| 170 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 3.211 ms | 0 - 52 MB | NPU
|
| 171 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.803 ms | 0 - 3 MB | NPU
|
| 172 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® SA8775P | 1.422 ms | 0 - 54 MB | NPU
|
| 173 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® QCS9075 | 1.227 ms | 0 - 10 MB | NPU
|
| 174 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.182 ms | 0 - 75 MB | NPU
|
| 175 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® SA7255P | 3.211 ms | 0 - 52 MB | NPU
|
| 176 |
+
| PoseLandmarkDetector | TFLITE | float | Qualcomm® SA8295P | 1.402 ms | 0 - 40 MB | NPU
|
| 177 |
+
| PoseLandmarkDetector | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.497 ms | 0 - 44 MB | NPU
|
| 178 |
+
| PoseLandmarkDetector | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.418 ms | 0 - 55 MB | NPU
|
| 179 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.215 ms | 0 - 51 MB | NPU
|
| 180 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCS6490 | 1.009 ms | 0 - 9 MB | NPU
|
| 181 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 0.828 ms | 0 - 38 MB | NPU
|
| 182 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.306 ms | 0 - 2 MB | NPU
|
| 183 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® SA8775P | 0.526 ms | 0 - 42 MB | NPU
|
| 184 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.474 ms | 0 - 6 MB | NPU
|
| 185 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.572 ms | 0 - 33 MB | NPU
|
| 186 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.445 ms | 0 - 55 MB | NPU
|
| 187 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® SA7255P | 0.828 ms | 0 - 38 MB | NPU
|
| 188 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Qualcomm® SA8295P | 0.722 ms | 0 - 36 MB | NPU
|
| 189 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.195 ms | 0 - 34 MB | NPU
|
| 190 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.312 ms | 0 - 34 MB | NPU
|
| 191 |
+
| PoseLandmarkDetector | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.168 ms | 0 - 41 MB | NPU
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| 192 |
|
| 193 |
## License
|
| 194 |
* The license for the original implementation of MediaPipe-Pose-Estimation can be found
|
| 195 |
[here](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE).
|
| 196 |
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| 197 |
## References
|
| 198 |
* [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/abs/2006.10204)
|
| 199 |
* [Source Model Implementation](https://github.com/zmurez/MediaPipePyTorch/)
|
| 200 |
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| 201 |
## Community
|
| 202 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 203 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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tool-versions.yaml
DELETED
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@@ -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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