---
library_name: pytorch
license: other
tags:
- backbone
- bu_auto
- 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 Qualcomm Devices

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 is based on the implementation of Unet-Segmentation found [here](https://github.com/milesial/Pytorch-UNet).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/unet_segmentation) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.51.0/unet_segmentation-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[Unet-Segmentation on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/unet_segmentation)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/unet_segmentation) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [Unet-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/unet_segmentation) for usage instructions.

## Model Details

**Model Type:** Model_use_case.semantic_segmentation

**Model Stats:**
- Model checkpoint: unet_carvana_scale1.0_epoch2
- Input resolution: 640x1280
- Number of output classes: 2 (foreground / background)
- Number of parameters: 31.0M
- Model size (float): 118 MB
- Model size (w8a8): 29.8 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 70.44 ms | 4 - 327 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® X2 Elite | 74.915 ms | 53 - 53 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® X Elite | 139.411 ms | 53 - 53 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 109.234 ms | 1 - 535 MB | NPU
| Unet-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 147.212 ms | 0 - 57 MB | NPU
| Unet-Segmentation | ONNX | float | Qualcomm® QCS9075 | 254.754 ms | 9 - 21 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 88.283 ms | 14 - 331 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 16.467 ms | 5 - 190 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X2 Elite | 20.098 ms | 29 - 29 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X Elite | 39.086 ms | 29 - 29 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 30.356 ms | 6 - 340 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS6490 | 4672.705 ms | 935 - 992 MB | CPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 39.83 ms | 0 - 2 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS9075 | 35.615 ms | 4 - 7 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCM6690 | 4131.985 ms | 838 - 845 MB | CPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 24.658 ms | 3 - 188 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 3886.044 ms | 841 - 848 MB | CPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 64.02 ms | 9 - 352 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® X2 Elite | 71.844 ms | 9 - 9 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® X Elite | 132.417 ms | 9 - 9 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 102.267 ms | 9 - 523 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 953.513 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 136.064 ms | 10 - 12 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8775P | 240.417 ms | 1 - 324 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS9075 | 248.027 ms | 9 - 27 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 274.324 ms | 4 - 539 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA7255P | 953.513 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8295P | 274.425 ms | 0 - 322 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 82.166 ms | 9 - 341 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 15.729 ms | 2 - 200 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 18.837 ms | 2 - 2 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X Elite | 35.624 ms | 2 - 2 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.142 ms | 2 - 321 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 267.917 ms | 2 - 8 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 121.445 ms | 2 - 181 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 34.992 ms | 2 - 4 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8775P | 32.176 ms | 2 - 182 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 34.328 ms | 1 - 6 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1216.249 ms | 2 - 522 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 54.75 ms | 2 - 319 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA7255P | 121.445 ms | 2 - 181 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8295P | 63.759 ms | 0 - 181 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 21.677 ms | 2 - 189 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.562 ms | 2 - 269 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 66.072 ms | 6 - 350 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 102.179 ms | 6 - 578 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 953.476 ms | 1 - 324 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 137.156 ms | 6 - 106 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8775P | 240.507 ms | 6 - 330 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 248.667 ms | 0 - 80 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 278.082 ms | 0 - 579 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA7255P | 953.476 ms | 1 - 324 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8295P | 274.486 ms | 6 - 328 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 82.488 ms | 5 - 337 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 15.814 ms | 1 - 197 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.4 ms | 1 - 318 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS6490 | 267.901 ms | 0 - 40 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 121.581 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 34.891 ms | 2 - 623 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8775P | 32.218 ms | 0 - 179 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS9075 | 34.236 ms | 0 - 36 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCM6690 | 1239.782 ms | 0 - 520 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 58.379 ms | 2 - 318 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA7255P | 121.581 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8295P | 63.762 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 21.753 ms | 2 - 188 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.581 ms | 2 - 267 MB | NPU

## License
* The license for the original implementation of Unet-Segmentation 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).