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---
library_name: pytorch
license: apache-2.0
pipeline_tag: object-detection
tags:
- real_time
- quantized
- android

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_face_quantized/web-assets/model_demo.png)

# MediaPipe-Face-Detection-Quantized: Optimized for Mobile Deployment
## Detect faces and locate facial features in real-time video and image streams

Designed for sub-millisecond processing, this model predicts bounding boxes and pose skeletons (left eye, right eye, nose tip, mouth, left eye tragion, and right eye tragion) of faces in an image.

This model is an implementation of MediaPipe-Face-Detection-Quantized found [here](https://github.com/zmurez/MediaPipePyTorch/).
This repository provides scripts to run MediaPipe-Face-Detection-Quantized on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/mediapipe_face_quantized).


### Model Details

- **Model Type:** Object detection
- **Model Stats:**
  - Input resolution: 256x256
  - Number of output classes: 6
  - Number of parameters (MediaPipeFaceDetector): 135K
  - Model size (MediaPipeFaceDetector): 255 KB
  - Number of parameters (MediaPipeFaceLandmarkDetector): 603K
  - Model size (MediaPipeFaceLandmarkDetector): 746 KB




| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
| ---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.25 ms | 0 - 1 MB | FP16 | NPU |  [MediaPipeFaceDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Face-Detection-Quantized/blob/main/MediaPipeFaceDetector.tflite) 
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.153 ms | 0 - 36 MB | FP16 | NPU |  [MediaPipeFaceLandmarkDetector.tflite](https://huggingface.co/qualcomm/MediaPipe-Face-Detection-Quantized/blob/main/MediaPipeFaceLandmarkDetector.tflite) 
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.295 ms | 0 - 45 MB | FP16 | NPU |  [MediaPipeFaceDetector.so](https://huggingface.co/qualcomm/MediaPipe-Face-Detection-Quantized/blob/main/MediaPipeFaceDetector.so) 
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.208 ms | 0 - 10 MB | FP16 | NPU |  [MediaPipeFaceLandmarkDetector.so](https://huggingface.co/qualcomm/MediaPipe-Face-Detection-Quantized/blob/main/MediaPipeFaceLandmarkDetector.so) 



## 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.mediapipe_face_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.mediapipe_face_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.mediapipe_face_quantized.export
```

```
Profile Job summary of MediaPipeFaceDetector
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 0.41 ms
Estimated Peak Memory Range: 0.53-0.53 MB
Compute Units: NPU (118) | Total (118)

Profile Job summary of MediaPipeFaceLandmarkDetector
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 0.38 ms
Estimated Peak Memory Range: 0.60-0.60 MB
Compute Units: NPU (112) | Total (112)


```





## 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 MediaPipe-Face-Detection-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_face_quantized).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)

## License
- The license for the original implementation of MediaPipe-Face-Detection-Quantized can be found
  [here](https://github.com/zmurez/MediaPipePyTorch/blob/master/LICENSE).
- 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
* [BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://arxiv.org/abs/1907.05047)
* [Source Model Implementation](https://github.com/zmurez/MediaPipePyTorch/)

## 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).