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---
datasets:
- coco
library_name: pytorch
license: apache-2.0
pipeline_tag: keypoint-detection
tags:
- quantized
- android
---
# Posenet-Mobilenet-Quantized: Optimized for Mobile Deployment
## Quantized human pose estimator
Posenet performs pose estimation on human images.
This model is an implementation of Posenet-Mobilenet-Quantized found [here]({source_repo}).
This repository provides scripts to run Posenet-Mobilenet-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized).
### Model Details
- **Model Type:** Pose estimation
- **Model Stats:**
- Model checkpoint: mobilenet_v1_101
- Input resolution: 513x257
- Number of parameters: 3.31M
- Model size: 3.47 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Posenet-Mobilenet-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.558 ms | 0 - 2 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.64 ms | 0 - 11 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.so](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.so) |
| Posenet-Mobilenet-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.48 ms | 0 - 47 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.445 ms | 0 - 18 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.so](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.so) |
| Posenet-Mobilenet-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 2.182 ms | 0 - 27 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 2.902 ms | 0 - 8 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 12.597 ms | 0 - 12 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.551 ms | 0 - 1 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.555 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.56 ms | 0 - 106 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.561 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.557 ms | 0 - 17 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 0.56 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.559 ms | 0 - 3 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.561 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 0.714 ms | 0 - 50 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 0.794 ms | 0 - 22 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.412 ms | 0 - 26 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) |
| Posenet-Mobilenet-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.484 ms | 0 - 17 MB | INT8 | NPU | Use Export Script |
| Posenet-Mobilenet-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.679 ms | 0 - 0 MB | INT8 | NPU | Use Export Script |
## Installation
This model can be installed as a Python package via pip.
```bash
pip install qai-hub-models
```
## 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.posenet_mobilenet_quantized.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.posenet_mobilenet_quantized.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.posenet_mobilenet_quantized.export
```
```
Profiling Results
------------------------------------------------------------
Posenet-Mobilenet-Quantized
Device : Samsung Galaxy S23 (13)
Runtime : TFLITE
Estimated inference time (ms) : 0.6
Estimated peak memory usage (MB): [0, 2]
Total # Ops : 48
Compute Unit(s) : NPU (48 ops)
```
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.posenet_mobilenet_quantized.demo --on-device
```
**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.posenet_mobilenet_quantized.demo -- --on-device
```
## 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 Posenet-Mobilenet-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Posenet-Mobilenet-Quantized can be found [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt).
* 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
* [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225)
* [Source Model Implementation](https://github.com/rwightman/posenet-pytorch)
## 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).
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