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Unet-Segmentation

Image Segmentation
PyTorch
LiteRT
ONNX
backbone
real_time
android
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Unet-Segmentation / README.md
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 ---
library_name: pytorch
license: other
tags:
- backbone
- real_time
- android
pipeline_tag: image-segmentation
---
![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/web-assets/model_demo.png)
# Unet-Segmentation: Optimized for Mobile Deployment
## Real-time segmentation optimized for mobile and edge
UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
This model is an implementation of Unet-Segmentation found [here](https://github.com/milesial/Pytorch-UNet).
This repository provides scripts to run Unet-Segmentation on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/unet_segmentation).
### Model Details
- **Model Type:** Model_use_case.semantic_segmentation
- **Model Stats:**
- Model checkpoint: unet_carvana_scale1.0_epoch2
- Input resolution: 224x224
- Number of output classes: 2 (foreground / background)
- Number of parameters: 31.0M
- Model size (float): 118 MB
- Model size (w8a8): 29.8 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Unet-Segmentation | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 952.953 ms | 0 - 98 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 946.447 ms | 0 - 110 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 266.695 ms | 6 - 162 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 279.553 ms | 9 - 130 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 156.062 ms | 6 - 465 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 145.554 ms | 9 - 54 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 253.329 ms | 6 - 104 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 249.775 ms | 0 - 110 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 952.953 ms | 0 - 98 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 946.447 ms | 0 - 110 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 158.946 ms | 3 - 467 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 149.352 ms | 9 - 54 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 280.575 ms | 6 - 107 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 276.093 ms | 2 - 113 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 152.13 ms | 6 - 472 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 152.947 ms | 10 - 54 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 253.329 ms | 6 - 104 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 249.775 ms | 0 - 110 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 151.859 ms | 6 - 466 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 151.338 ms | 9 - 55 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 158.003 ms | 13 - 156 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
| Unet-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 115.373 ms | 4 - 136 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 113.677 ms | 9 - 118 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 118.521 ms | 21 - 129 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
| Unet-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 107.435 ms | 5 - 104 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.tflite) |
| Unet-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 93.485 ms | 18 - 132 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 108.739 ms | 21 - 129 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
| Unet-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 145.843 ms | 83 - 83 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.dlc) |
| Unet-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 151.145 ms | 53 - 53 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation.onnx) |
| Unet-Segmentation | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 127.512 ms | 2 - 49 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 1393.003 ms | 2 - 62 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 54.66 ms | 2 - 89 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 57.551 ms | 2 - 100 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 38.472 ms | 2 - 867 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 583.061 ms | 2 - 19 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 35.487 ms | 2 - 48 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 679.256 ms | 0 - 60 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 308.485 ms | 2 - 262 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 332.825 ms | 2 - 268 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 3126.186 ms | 0 - 847 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 127.512 ms | 2 - 49 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 1393.003 ms | 2 - 62 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 37.675 ms | 1 - 867 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 583.468 ms | 2 - 21 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 68.171 ms | 2 - 49 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 64.786 ms | 0 - 61 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 38.154 ms | 2 - 863 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 583.569 ms | 2 - 28 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 35.487 ms | 2 - 48 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 679.256 ms | 0 - 60 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 37.638 ms | 2 - 865 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 583.143 ms | 2 - 20 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 130.225 ms | 0 - 150 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.onnx) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 28.733 ms | 0 - 88 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 449.851 ms | 2 - 102 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 111.239 ms | 29 - 701 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.onnx) |
| Unet-Segmentation | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 24.08 ms | 1 - 52 MB | NPU | [Unet-Segmentation.tflite](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.tflite) |
| Unet-Segmentation | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 519.786 ms | 6 - 74 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 91.315 ms | 1 - 749 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.onnx) |
| Unet-Segmentation | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 615.428 ms | 67 - 67 MB | NPU | [Unet-Segmentation.dlc](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.dlc) |
| Unet-Segmentation | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 156.351 ms | 39 - 39 MB | NPU | [Unet-Segmentation.onnx](https://huggingface.co/qualcomm/Unet-Segmentation/blob/main/Unet-Segmentation_w8a8.onnx) |
## Installation
Install the 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.unet_segmentation.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.unet_segmentation.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.unet_segmentation.export
```
```
Profiling Results
------------------------------------------------------------
Unet-Segmentation
Device : cs_8275 (ANDROID 14)
Runtime : TFLITE
Estimated inference time (ms) : 953.0
Estimated peak memory usage (MB): [0, 98]
Total # Ops : 32
Compute Unit(s) : npu (32 ops) gpu (0 ops) cpu (0 ops)
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/unet_segmentation/qai_hub_models/models/Unet-Segmentation/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.unet_segmentation import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# 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. [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.unet_segmentation.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.unet_segmentation.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 Unet-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/unet_segmentation).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Unet-Segmentation can be found
[here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/milesial/Pytorch-UNet/blob/master/LICENSE)
## References
* [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
* [Source Model Implementation](https://github.com/milesial/Pytorch-UNet)
## 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).