Hugging Face's logo

qualcomm
/
SINet

Image Segmentation
PyTorch
LiteRT
android
Model card Files
xet
Community
SINet / README.md
qaihm-bot's picture
qaihm-bot
v0.45.0
db84178 verified
preview code
|
raw
history blame contribute delete
19.6 kB
 ---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-segmentation
---
![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/sinet/web-assets/model_demo.png)
# 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](https://github.com/clovaai/ext_portrait_segmentation).
This repository provides scripts to run SINet on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/sinet).
### Model Details
- **Model Type:** Model_use_case.semantic_segmentation
- **Model Stats:**
- Model checkpoint: SINet.pth
- Input resolution: 224x224
- Number of output classes: 2 (foreground / background)
- Number of parameters: 91.9K
- Model size (float): 415 KB
- Model size (w8a8): 241 KB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| SINet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.518 ms | 0 - 127 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.505 ms | 1 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.35 ms | 0 - 147 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.335 ms | 1 - 146 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.584 ms | 0 - 2 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.603 ms | 1 - 4 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1.616 ms | 0 - 3 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet.onnx.zip) |
| SINet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 2.007 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 8.327 ms | 1 - 126 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.518 ms | 0 - 127 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.505 ms | 1 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.43 ms | 0 - 136 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.415 ms | 0 - 135 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 2.007 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 8.327 ms | 1 - 126 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.048 ms | 0 - 146 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.046 ms | 1 - 146 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.055 ms | 0 - 120 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet.onnx.zip) |
| SINet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 0.773 ms | 0 - 130 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.786 ms | 0 - 129 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 0.82 ms | 0 - 104 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet.onnx.zip) |
| SINet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 0.639 ms | 0 - 129 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet.tflite) |
| SINet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 0.648 ms | 0 - 129 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 0.739 ms | 0 - 104 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet.onnx.zip) |
| SINet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.85 ms | 1 - 1 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet.dlc) |
| SINet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.605 ms | 2 - 2 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet.onnx.zip) |
| SINet | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.497 ms | 0 - 127 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.29 ms | 0 - 153 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.809 ms | 0 - 3 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 2.162 ms | 0 - 127 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.497 ms | 0 - 127 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.865 ms | 0 - 135 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 2.162 ms | 0 - 127 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.227 ms | 0 - 150 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.914 ms | 0 - 131 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 0.734 ms | 0 - 130 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.033 ms | 0 - 0 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a16.dlc) |
| SINet | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | TFLITE | 14.496 ms | 0 - 128 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | ONNX | 12.289 ms | 7 - 23 MB | CPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 19.842 ms | 0 - 11 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 31.545 ms | 7 - 11 MB | CPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 2.483 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 2.505 ms | 0 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1.383 ms | 0 - 142 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.428 ms | 0 - 146 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.219 ms | 0 - 3 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.291 ms | 0 - 2 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 5.133 ms | 5 - 8 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.53 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 1.543 ms | 0 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 2.483 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 2.505 ms | 0 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 1.876 ms | 0 - 133 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 1.952 ms | 0 - 133 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.53 ms | 0 - 125 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 1.543 ms | 0 - 125 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.848 ms | 0 - 147 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.875 ms | 0 - 146 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.489 ms | 0 - 127 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 0.633 ms | 0 - 129 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.657 ms | 0 - 129 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 2.939 ms | 0 - 107 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 7.435 ms | 0 - 128 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 11.154 ms | 7 - 23 MB | CPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 0.564 ms | 0 - 129 MB | NPU | [SINet.tflite](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.tflite) |
| SINet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 0.529 ms | 0 - 127 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 2.782 ms | 0 - 107 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
| SINet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.499 ms | 0 - 0 MB | NPU | [SINet.dlc](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.dlc) |
| SINet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.427 ms | 6 - 6 MB | NPU | [SINet.onnx.zip](https://huggingface.co/qualcomm/SINet/blob/main/SINet_w8a8.onnx.zip) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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.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.
```bash
python -m qai_hub_models.models.sinet.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/sinet/qai_hub_models/models/SINet/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.sinet import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S25")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = 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.
```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 Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.sinet.demo --eval-mode 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 -- --eval-mode on-device
```
## 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 SINet's performance across various devices [here](https://aihub.qualcomm.com/models/sinet).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of SINet can be found
[here](https://github.com/clovaai/ext_portrait_segmentation/blob/master/LICENSE).
## References
* [SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder](https://arxiv.org/abs/1911.09099)
* [Source Model Implementation](https://github.com/clovaai/ext_portrait_segmentation)
## 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).