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MediaPipe-Face-Detection

Object Detection
PyTorch
TF Lite
ONNX
real_time
android
Model card Files Community
1

MediaPipe-Face-Detection: 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 found here.

This repository provides scripts to run MediaPipe-Face-Detection on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Object detection
  • Model Stats:
    • Input resolution: 256x256
    • Number of parameters (MediaPipeFaceDetector): 135K
    • Model size (MediaPipeFaceDetector): 565 KB
    • Number of parameters (MediaPipeFaceLandmarkDetector): 603K
    • Model size (MediaPipeFaceLandmarkDetector): 2.34 MB
    • Number of output classes: 6
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
MediaPipeFaceDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 0.559 ms 0 - 37 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 0.632 ms 1 - 39 MB FP16 NPU MediaPipe-Face-Detection.so
MediaPipeFaceDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 1.055 ms 1 - 2 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 0.404 ms 0 - 20 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 0.454 ms 0 - 14 MB FP16 NPU MediaPipe-Face-Detection.so
MediaPipeFaceDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 0.739 ms 0 - 42 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 0.417 ms 0 - 12 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 0.386 ms 0 - 12 MB FP16 NPU Use Export Script
MediaPipeFaceDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 0.759 ms 0 - 31 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceDetector QCS8550 (Proxy) QCS8550 Proxy TFLITE 0.548 ms 0 - 76 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector QCS8550 (Proxy) QCS8550 Proxy QNN 0.603 ms 1 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceDetector SA7255P ADP SA7255P TFLITE 18.827 ms 0 - 16 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector SA7255P ADP SA7255P QNN 19.157 ms 1 - 11 MB FP16 NPU Use Export Script
MediaPipeFaceDetector SA8255 (Proxy) SA8255P Proxy TFLITE 0.555 ms 0 - 5 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector SA8255 (Proxy) SA8255P Proxy QNN 0.607 ms 1 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceDetector SA8295P ADP SA8295P TFLITE 1.141 ms 0 - 10 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector SA8295P ADP SA8295P QNN 1.221 ms 0 - 6 MB FP16 NPU Use Export Script
MediaPipeFaceDetector SA8650 (Proxy) SA8650P Proxy TFLITE 0.553 ms 0 - 5 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector SA8650 (Proxy) SA8650P Proxy QNN 0.617 ms 1 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceDetector SA8775P ADP SA8775P TFLITE 1.273 ms 0 - 17 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector SA8775P ADP SA8775P QNN 1.468 ms 1 - 7 MB FP16 NPU Use Export Script
MediaPipeFaceDetector QCS8450 (Proxy) QCS8450 Proxy TFLITE 0.766 ms 0 - 14 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceDetector QCS8450 (Proxy) QCS8450 Proxy QNN 0.827 ms 1 - 15 MB FP16 NPU Use Export Script
MediaPipeFaceDetector Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.748 ms 1 - 1 MB FP16 NPU Use Export Script
MediaPipeFaceDetector Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.036 ms 2 - 2 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceLandmarkDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 0.196 ms 0 - 8 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 0.278 ms 0 - 8 MB FP16 NPU MediaPipe-Face-Detection.so
MediaPipeFaceLandmarkDetector Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 0.51 ms 0 - 27 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceLandmarkDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 0.147 ms 0 - 11 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 0.211 ms 0 - 11 MB FP16 NPU MediaPipe-Face-Detection.so
MediaPipeFaceLandmarkDetector Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 0.399 ms 0 - 32 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceLandmarkDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 0.123 ms 0 - 10 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 0.209 ms 0 - 10 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 0.409 ms 0 - 19 MB FP16 NPU MediaPipe-Face-Detection.onnx
MediaPipeFaceLandmarkDetector QCS8550 (Proxy) QCS8550 Proxy TFLITE 0.199 ms 0 - 7 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector QCS8550 (Proxy) QCS8550 Proxy QNN 0.275 ms 0 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector SA7255P ADP SA7255P TFLITE 3.672 ms 0 - 12 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector SA7255P ADP SA7255P QNN 3.963 ms 0 - 10 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector SA8255 (Proxy) SA8255P Proxy TFLITE 0.195 ms 0 - 7 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector SA8255 (Proxy) SA8255P Proxy QNN 0.287 ms 0 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector SA8295P ADP SA8295P TFLITE 0.576 ms 0 - 8 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector SA8295P ADP SA8295P QNN 0.791 ms 0 - 6 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector SA8650 (Proxy) SA8650P Proxy TFLITE 0.191 ms 0 - 7 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector SA8650 (Proxy) SA8650P Proxy QNN 0.28 ms 0 - 2 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector SA8775P ADP SA8775P TFLITE 0.5 ms 0 - 13 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector SA8775P ADP SA8775P QNN 0.8 ms 0 - 6 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector QCS8450 (Proxy) QCS8450 Proxy TFLITE 0.275 ms 0 - 13 MB FP16 NPU MediaPipe-Face-Detection.tflite
MediaPipeFaceLandmarkDetector QCS8450 (Proxy) QCS8450 Proxy QNN 0.377 ms 0 - 16 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.379 ms 0 - 0 MB FP16 NPU Use Export Script
MediaPipeFaceLandmarkDetector Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.51 ms 3 - 3 MB FP16 NPU MediaPipe-Face-Detection.onnx

Installation

This model can be installed as a Python package via pip.

pip install qai-hub-models

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

python -m qai_hub_models.models.mediapipe_face.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.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.
python -m qai_hub_models.models.mediapipe_face.export

Profiling Results
------------------------------------------------------------
MediaPipeFaceDetector
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 0.6                    
Estimated peak memory usage (MB): [0, 37]                
Total # Ops                     : 111                    
Compute Unit(s)                 : NPU (111 ops)          

------------------------------------------------------------
MediaPipeFaceLandmarkDetector
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 0.2                    
Estimated peak memory usage (MB): [0, 8]                 
Total # Ops                     : 100                    
Compute Unit(s)                 : NPU (100 ops)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.mediapipe_face import Model

# Load the model
model = Model.from_pretrained()
face_detector_model = model.face_detector
face_landmark_detector_model = model.face_landmark_detector

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
face_detector_input_shape = face_detector_model.get_input_spec()
face_detector_sample_inputs = face_detector_model.sample_inputs()

traced_face_detector_model = torch.jit.trace(face_detector_model, [torch.tensor(data[0]) for _, data in face_detector_sample_inputs.items()])

# Compile model on a specific device
face_detector_compile_job = hub.submit_compile_job(
    model=traced_face_detector_model ,
    device=device,
    input_specs=face_detector_model.get_input_spec(),
)

# Get target model to run on-device
face_detector_target_model = face_detector_compile_job.get_target_model()
# Trace model
face_landmark_detector_input_shape = face_landmark_detector_model.get_input_spec()
face_landmark_detector_sample_inputs = face_landmark_detector_model.sample_inputs()

traced_face_landmark_detector_model = torch.jit.trace(face_landmark_detector_model, [torch.tensor(data[0]) for _, data in face_landmark_detector_sample_inputs.items()])

# Compile model on a specific device
face_landmark_detector_compile_job = hub.submit_compile_job(
    model=traced_face_landmark_detector_model ,
    device=device,
    input_specs=face_landmark_detector_model.get_input_spec(),
)

# Get target model to run on-device
face_landmark_detector_target_model = face_landmark_detector_compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

face_detector_profile_job = hub.submit_profile_job(
    model=face_detector_target_model,
    device=device,
)
face_landmark_detector_profile_job = hub.submit_profile_job(
    model=face_landmark_detector_target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

face_detector_input_data = face_detector_model.sample_inputs()
face_detector_inference_job = hub.submit_inference_job(
    model=face_detector_target_model,
    device=device,
    inputs=face_detector_input_data,
)
face_detector_inference_job.download_output_data()
face_landmark_detector_input_data = face_landmark_detector_model.sample_inputs()
face_landmark_detector_inference_job = hub.submit_inference_job(
    model=face_landmark_detector_target_model,
    device=device,
    inputs=face_landmark_detector_input_data,
)
face_landmark_detector_inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app 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's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of MediaPipe-Face-Detection can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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