--- library_name: pytorch license: bsd-3-clause pipeline_tag: object-detection tags: - real_time - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/web-assets/model_demo.png) # FaceAttribNet: Optimized for Mobile Deployment ## Comprehensive facial analysis by extracting face features Facial feature extraction and additional attributes including liveness, eyeclose, mask and glasses detection for face recognition. This model is an implementation of FaceAttribNet found [here](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/face_attrib_net/model.py). This repository provides scripts to run FaceAttribNet on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/face_attrib_net). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Model checkpoint: multitask_FR_state_dict.pt - Input resolution: 128x128 - Number of parameters: 11.6M - Model size: 47.6MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | FaceAttribNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.957 ms | 0 - 2 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.041 ms | 0 - 8 MB | FP16 | NPU | [FaceAttribNet.so](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.so) | | FaceAttribNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.2 ms | 0 - 364 MB | FP16 | NPU | [FaceAttribNet.onnx](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.onnx) | | FaceAttribNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.71 ms | 0 - 110 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.781 ms | 0 - 20 MB | FP16 | NPU | [FaceAttribNet.so](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.so) | | FaceAttribNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.907 ms | 0 - 113 MB | FP16 | NPU | [FaceAttribNet.onnx](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.onnx) | | FaceAttribNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.658 ms | 0 - 60 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.702 ms | 0 - 21 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.76 ms | 0 - 61 MB | FP16 | NPU | [FaceAttribNet.onnx](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.onnx) | | FaceAttribNet | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.947 ms | 0 - 1 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.998 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.977 ms | 0 - 2 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.001 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 0.978 ms | 0 - 2 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | SA8775 (Proxy) | SA8775P Proxy | QNN | 0.998 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.987 ms | 0 - 2 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.996 ms | 0 - 2 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | SA8295P ADP | SA8295P | TFLITE | 1.663 ms | 0 - 52 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | SA8295P ADP | SA8295P | QNN | 1.742 ms | 0 - 6 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.258 ms | 0 - 102 MB | FP16 | NPU | [FaceAttribNet.tflite](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.tflite) | | FaceAttribNet | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.356 ms | 0 - 21 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.14 ms | 0 - 0 MB | FP16 | NPU | Use Export Script | | FaceAttribNet | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.213 ms | 27 - 27 MB | FP16 | NPU | [FaceAttribNet.onnx](https://huggingface.co/qualcomm/FaceAttribNet/blob/main/FaceAttribNet.onnx) | ## 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.face_attrib_net.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.face_attrib_net.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.face_attrib_net.export ``` ``` Profiling Results ------------------------------------------------------------ FaceAttribNet Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 1.0 Estimated peak memory usage (MB): [0, 2] Total # Ops : 161 Compute Unit(s) : NPU (161 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/face_attrib_net/qai_hub_models/models/FaceAttribNet/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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. ```python import torch import qai_hub as hub from qai_hub_models.models.face_attrib_net import # Load the model # Device device = hub.Device("Samsung Galaxy S23") ``` 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. ```python profile_job = hub.submit_profile_job( model=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. ```python input_data = torch_model.sample_inputs() inference_job = hub.submit_inference_job( model=target_model, device=device, inputs=input_data, ) on_device_output = 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](https://myaccount.qualcomm.com/signup). ## 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 FaceAttribNet's performance across various devices [here](https://aihub.qualcomm.com/models/face_attrib_net). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of FaceAttribNet can be found [here](https://github.com/qcom-ai-hub/ai-hub-models-internal/blob/main/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 * [None](None) * [Source Model Implementation](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/face_attrib_net/model.py) ## 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).