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--- |
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library_name: pytorch |
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license: apache-2.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- android |
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--- |
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# MediaPipe-Selfie-Segmentation: Optimized for Mobile Deployment |
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## Segments the person from background in a selfie image and realtime background segmentation in video conferencing |
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Light-weight model that segments a person from the background in square or landscape selfie and video conference imagery. |
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This model is an implementation of MediaPipe-Selfie-Segmentation found [here](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation). |
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This repository provides scripts to run MediaPipe-Selfie-Segmentation on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/mediapipe_selfie). |
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### Model Details |
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- **Model Type:** Semantic segmentation |
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- **Model Stats:** |
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- Model checkpoint: Square |
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- Input resolution (Square): 256x256 |
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- Input resolution (Landscape): 144x256 |
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- Number of parameters: 106K |
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- Model size: 454 KB |
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- Number of output classes: 6 |
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.706 ms | 0 - 40 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.777 ms | 1 - 6 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.so](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.so) | |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.331 ms | 0 - 2 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.onnx](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.onnx) | |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.468 ms | 0 - 13 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.518 ms | 0 - 14 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.so](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.so) | |
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| MediaPipe-Selfie-Segmentation | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.883 ms | 0 - 36 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.onnx](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.onnx) | |
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| MediaPipe-Selfie-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.441 ms | 0 - 10 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.483 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.872 ms | 0 - 25 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.onnx](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.onnx) | |
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| MediaPipe-Selfie-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.694 ms | 0 - 4 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.758 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | SA7255P ADP | SA7255P | TFLITE | 6.211 ms | 0 - 11 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | SA7255P ADP | SA7255P | QNN | 6.499 ms | 1 - 6 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.706 ms | 0 - 7 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.785 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | SA8295P ADP | SA8295P | TFLITE | 1.28 ms | 0 - 9 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | SA8295P ADP | SA8295P | QNN | 1.472 ms | 0 - 6 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.703 ms | 0 - 68 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.761 ms | 0 - 2 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | SA8775P ADP | SA8775P | TFLITE | 1.197 ms | 0 - 9 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | SA8775P ADP | SA8775P | QNN | 1.426 ms | 1 - 6 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 0.935 ms | 0 - 14 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.tflite](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.tflite) | |
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| MediaPipe-Selfie-Segmentation | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 0.987 ms | 1 - 16 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.904 ms | 1 - 1 MB | FP16 | NPU | Use Export Script | |
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| MediaPipe-Selfie-Segmentation | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 5.938 ms | 2 - 2 MB | FP16 | NPU | [MediaPipe-Selfie-Segmentation.onnx](https://huggingface.co/qualcomm/MediaPipe-Selfie-Segmentation/blob/main/MediaPipe-Selfie-Segmentation.onnx) | |
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## Installation |
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This model can be installed as a Python package via pip. |
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```bash |
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pip install "qai-hub-models[mediapipe_selfie]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.mediapipe_selfie.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_selfie.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_selfie.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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MediaPipe-Selfie-Segmentation |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 0.7 |
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Estimated peak memory usage (MB): [0, 40] |
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Total # Ops : 118 |
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Compute Unit(s) : NPU (118 ops) |
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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_selfie/qai_hub_models/models/MediaPipe-Selfie-Segmentation/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_selfie 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 S23") |
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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. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Run demo on a cloud-hosted device |
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You can also run the demo on-device. |
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```bash |
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python -m qai_hub_models.models.mediapipe_selfie.demo --on-device |
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``` |
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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_selfie.demo -- --on-device |
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``` |
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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-Selfie-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/mediapipe_selfie). |
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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-Selfie-Segmentation can be found [here](https://github.com/google/mediapipe/blob/master/LICENSE). |
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* 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) |
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## References |
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* [Image segmentation guide](https://developers.google.com/mediapipe/solutions/vision/image_segmenter/) |
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* [Source Model Implementation](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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