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Unet-Segmentation

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---
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/v0.53.1/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/v0.53.1/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/v0.53.1/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/v0.53.1/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/v0.53.1/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/v0.53.1/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 | 69.648 ms | 5 - 328 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite Mobile | 90.468 ms | 15 - 333 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® X2 Elite | 75.285 ms | 53 - 53 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® X Elite | 139.427 ms | 53 - 53 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® X Elite | 139.427 ms | 53 - 53 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 110.568 ms | 2 - 537 MB | NPU
| Unet-Segmentation | ONNX | float | Qualcomm® QCS8550 (Proxy) | 160.361 ms | 0 - 452 MB | NPU
| Unet-Segmentation | ONNX | float | Qualcomm® QCS9075 | 254.769 ms | 9 - 21 MB | NPU
| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 90.468 ms | 15 - 333 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 16.43 ms | 0 - 184 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite Mobile | 24.865 ms | 3 - 191 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X2 Elite | 20.214 ms | 29 - 29 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X Elite | 39.064 ms | 29 - 29 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X Elite | 39.064 ms | 29 - 29 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 30.177 ms | 6 - 337 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS6490 | 4673.96 ms | 945 - 1002 MB | CPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 40.195 ms | 4 - 6 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS9075 | 35.651 ms | 4 - 7 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCM6690 | 4187.869 ms | 828 - 835 MB | CPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 24.865 ms | 3 - 191 MB | NPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 3889.111 ms | 842 - 850 MB | CPU
| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 3889.111 ms | 842 - 850 MB | CPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 61.809 ms | 9 - 352 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 82.364 ms | 9 - 342 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® X2 Elite | 71.862 ms | 9 - 9 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® X Elite | 132.376 ms | 9 - 9 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® X Elite | 132.376 ms | 9 - 9 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 103.692 ms | 8 - 522 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 954.479 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 142.471 ms | 9 - 857 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8775P | 240.426 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8775P | 240.426 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8775P | 240.426 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS9075 | 248.031 ms | 9 - 27 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 291.567 ms | 4 - 541 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA7255P | 954.479 ms | 0 - 323 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8295P | 274.454 ms | 0 - 322 MB | NPU
| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 82.364 ms | 9 - 342 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 15.753 ms | 2 - 198 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite Mobile | 21.891 ms | 2 - 188 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 18.848 ms | 2 - 2 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X Elite | 35.651 ms | 2 - 2 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X Elite | 35.651 ms | 2 - 2 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.104 ms | 2 - 319 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 267.641 ms | 2 - 8 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 121.54 ms | 1 - 180 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 35.022 ms | 2 - 5 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8775P | 32.194 ms | 1 - 180 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8775P | 32.194 ms | 1 - 180 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8775P | 32.194 ms | 1 - 180 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 34.633 ms | 1 - 6 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1202.116 ms | 2 - 522 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 58.446 ms | 2 - 318 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA7255P | 121.54 ms | 1 - 180 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8295P | 63.715 ms | 2 - 181 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 21.891 ms | 2 - 188 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.739 ms | 2 - 268 MB | NPU
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.739 ms | 2 - 268 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 66.075 ms | 6 - 345 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite Mobile | 82.297 ms | 5 - 336 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 105.484 ms | 5 - 575 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 953.329 ms | 0 - 323 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 146.708 ms | 6 - 107 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8775P | 240.513 ms | 6 - 330 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8775P | 240.513 ms | 6 - 330 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8775P | 240.513 ms | 6 - 330 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS9075 | 248.057 ms | 0 - 80 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 317.123 ms | 8 - 588 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA7255P | 953.329 ms | 0 - 323 MB | NPU
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8295P | 274.502 ms | 6 - 328 MB | NPU
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 82.297 ms | 5 - 336 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 15.882 ms | 2 - 197 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite Mobile | 21.907 ms | 1 - 188 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.167 ms | 1 - 315 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS6490 | 267.608 ms | 2 - 41 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 121.525 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 35.161 ms | 2 - 399 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8775P | 32.215 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8775P | 32.215 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8775P | 32.215 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS9075 | 34.207 ms | 0 - 36 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCM6690 | 1241.466 ms | 0 - 521 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 60.085 ms | 2 - 316 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA7255P | 121.525 ms | 2 - 180 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8295P | 63.734 ms | 2 - 181 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 21.907 ms | 1 - 188 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.528 ms | 0 - 264 MB | NPU
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.528 ms | 0 - 264 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).