SINet: Optimized for Mobile Deployment
Lightweight portrait segmentation for background removal
SINet is a machine learning model that is designed to segment people from close-up portrait images in real time.
This model is an implementation of SINet found here.
This repository provides scripts to run SINet on Qualcomm® devices. More details on model performance across various devices, can be found here.
Model Details
- Model Type: Semantic segmentation
- Model Stats:
- Model checkpoint: SINet.pth
- Input resolution: 224x224
- Number of parameters: 91.9K
- Model size: 430 KB
- Number of output classes: 2 (foreground / background)
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| SINet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.76 ms | 0 - 12 MB | FP16 | NPU | SINet.tflite |
| SINet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.185 ms | 1 - 6 MB | FP16 | NPU | SINet.so |
| SINet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 2.262 ms | 0 - 72 MB | FP16 | NPU | SINet.onnx |
| SINet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.138 ms | 0 - 31 MB | FP16 | NPU | SINet.tflite |
| SINet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.805 ms | 1 - 16 MB | FP16 | NPU | SINet.so |
| SINet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.495 ms | 0 - 36 MB | FP16 | NPU | SINet.onnx |
| SINet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.922 ms | 0 - 22 MB | FP16 | NPU | SINet.tflite |
| SINet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.77 ms | 0 - 12 MB | FP16 | NPU | Use Export Script |
| SINet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.25 ms | 0 - 25 MB | FP16 | NPU | SINet.onnx |
| SINet | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.732 ms | 0 - 2 MB | FP16 | NPU | SINet.tflite |
| SINet | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.159 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| SINet | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.75 ms | 0 - 1 MB | FP16 | NPU | SINet.tflite |
| SINet | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.173 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| SINet | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.753 ms | 0 - 1 MB | FP16 | NPU | SINet.tflite |
| SINet | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.172 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| SINet | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.749 ms | 0 - 1 MB | FP16 | NPU | SINet.tflite |
| SINet | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.166 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| SINet | SA8295P ADP | SA8295P | TFLITE | 2.404 ms | 0 - 21 MB | FP16 | NPU | SINet.tflite |
| SINet | SA8295P ADP | SA8295P | QNN | 2.114 ms | 0 - 6 MB | FP16 | NPU | Use Export Script |
| SINet | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 1.886 ms | 0 - 31 MB | FP16 | NPU | SINet.tflite |
| SINet | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 1.316 ms | 1 - 17 MB | FP16 | NPU | Use Export Script |
| SINet | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1.345 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
| SINet | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.36 ms | 2 - 2 MB | FP16 | NPU | SINet.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.sinet.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.sinet.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.sinet.export
Profiling Results
------------------------------------------------------------
SINet
Device : Samsung Galaxy S23 (13)
Runtime : TFLITE
Estimated inference time (ms) : 1.8
Estimated peak memory usage (MB): [0, 12]
Total # Ops : 240
Compute Unit(s) : NPU (240 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.sinet 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.
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.
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.
Run demo on a cloud-hosted device
You can also run the demo on-device.
python -m qai_hub_models.models.sinet.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.sinet.demo -- --on-device
Deploying compiled model to Android
The models can be deployed using multiple runtimes:
TensorFlow Lite (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application.QNN (
.soexport ): This sample app provides instructions on how to use the.soshared library in an Android application.
View on Qualcomm® AI Hub
Get more details on SINet's performance across various devices here. Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of SINet can be found here.
- The license for the compiled assets for on-device deployment can be found here
References
- SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
- Source Model Implementation
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.