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. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up 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 1.24.1	Download
ONNX	w8a8	Universal	QAIRT 2.42, ONNX Runtime 1.24.1	Download
QNN_DLC	float	Universal	QAIRT 2.43	Download
QNN_DLC	w8a8	Universal	QAIRT 2.43	Download
TFLITE	float	Universal	QAIRT 2.43, TFLite 2.17.0	Download
TFLITE	w8a8	Universal	QAIRT 2.43, TFLite 2.17.0	Download

For more device-specific assets and performance metrics, visit Unet-Segmentation on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models 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 for usage instructions.

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

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
Unet-Segmentation	ONNX	float	Snapdragon® X Elite	139.479 ms	53 - 53 MB	NPU
Unet-Segmentation	ONNX	float	Snapdragon® 8 Gen 3 Mobile	110.033 ms	2 - 538 MB	NPU
Unet-Segmentation	ONNX	float	Qualcomm® QCS8550 (Proxy)	149.746 ms	0 - 57 MB	NPU
Unet-Segmentation	ONNX	float	Qualcomm® QCS9075	254.819 ms	9 - 21 MB	NPU
Unet-Segmentation	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	88.65 ms	15 - 330 MB	NPU
Unet-Segmentation	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	70.141 ms	24 - 344 MB	NPU
Unet-Segmentation	ONNX	float	Snapdragon® X2 Elite	75.136 ms	53 - 53 MB	NPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® X Elite	39.056 ms	29 - 29 MB	NPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® 8 Gen 3 Mobile	30.222 ms	6 - 338 MB	NPU
Unet-Segmentation	ONNX	w8a8	Qualcomm® QCS6490	4647.573 ms	942 - 1000 MB	CPU
Unet-Segmentation	ONNX	w8a8	Qualcomm® QCS8550 (Proxy)	41.711 ms	4 - 6 MB	NPU
Unet-Segmentation	ONNX	w8a8	Qualcomm® QCS9075	35.615 ms	4 - 7 MB	NPU
Unet-Segmentation	ONNX	w8a8	Qualcomm® QCM6690	4142.982 ms	836 - 842 MB	CPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® 8 Elite For Galaxy Mobile	24.867 ms	3 - 191 MB	NPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® 7 Gen 4 Mobile	3886.757 ms	834 - 842 MB	CPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® 8 Elite Gen 5 Mobile	16.251 ms	0 - 185 MB	NPU
Unet-Segmentation	ONNX	w8a8	Snapdragon® X2 Elite	20.144 ms	29 - 29 MB	NPU
Unet-Segmentation	QNN_DLC	float	Snapdragon® X Elite	132.389 ms	9 - 9 MB	NPU
Unet-Segmentation	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	101.504 ms	9 - 547 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	953.65 ms	0 - 322 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	135.421 ms	10 - 13 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® SA8775P	240.476 ms	0 - 324 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® QCS9075	248.263 ms	9 - 27 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	274.543 ms	7 - 549 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® SA7255P	953.65 ms	0 - 322 MB	NPU
Unet-Segmentation	QNN_DLC	float	Qualcomm® SA8295P	274.502 ms	0 - 322 MB	NPU
Unet-Segmentation	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	82.334 ms	0 - 331 MB	NPU
Unet-Segmentation	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	65.76 ms	9 - 350 MB	NPU
Unet-Segmentation	QNN_DLC	float	Snapdragon® X2 Elite	71.586 ms	9 - 9 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® X Elite	35.715 ms	2 - 2 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® 8 Gen 3 Mobile	26.079 ms	2 - 321 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® QCS6490	267.761 ms	2 - 8 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® QCS8550 (Proxy)	35.233 ms	2 - 35 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® SA8775P	32.227 ms	1 - 180 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® QCS9075	34.981 ms	2 - 8 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® QCM6690	1232.21 ms	3 - 522 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® QCS8450 (Proxy)	57.992 ms	2 - 320 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Qualcomm® SA8295P	63.705 ms	0 - 180 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® 8 Elite For Galaxy Mobile	21.942 ms	2 - 191 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® 7 Gen 4 Mobile	78.791 ms	2 - 268 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® 8 Elite Gen 5 Mobile	15.788 ms	2 - 197 MB	NPU
Unet-Segmentation	QNN_DLC	w8a8	Snapdragon® X2 Elite	18.916 ms	2 - 2 MB	NPU
Unet-Segmentation	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	102.428 ms	0 - 535 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® QCS8275 (Proxy)	953.609 ms	0 - 325 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® QCS8550 (Proxy)	156.101 ms	6 - 443 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® SA8775P	240.55 ms	6 - 331 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® QCS9075	248.108 ms	0 - 80 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® QCS8450 (Proxy)	277.793 ms	7 - 552 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® SA7255P	953.609 ms	0 - 325 MB	NPU
Unet-Segmentation	TFLITE	float	Qualcomm® SA8295P	274.559 ms	6 - 329 MB	NPU
Unet-Segmentation	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	81.817 ms	4 - 337 MB	NPU
Unet-Segmentation	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	67.608 ms	5 - 351 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Snapdragon® 8 Gen 3 Mobile	26.281 ms	0 - 318 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCS6490	267.819 ms	0 - 40 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCS8275 (Proxy)	121.596 ms	2 - 181 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCS8550 (Proxy)	34.978 ms	0 - 651 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® SA8775P	32.263 ms	2 - 181 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCS9075	34.225 ms	1 - 38 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCM6690	1238.794 ms	0 - 519 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® QCS8450 (Proxy)	58.357 ms	2 - 321 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® SA7255P	121.596 ms	2 - 181 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Qualcomm® SA8295P	63.788 ms	2 - 180 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Snapdragon® 8 Elite For Galaxy Mobile	21.845 ms	1 - 187 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Snapdragon® 7 Gen 4 Mobile	78.641 ms	2 - 266 MB	NPU
Unet-Segmentation	TFLITE	w8a8	Snapdragon® 8 Elite Gen 5 Mobile	15.774 ms	7 - 202 MB	NPU

License

• The license for the original implementation of Unet-Segmentation can be found here.

References

• U-Net: Convolutional Networks for Biomedical Image Segmentation
• 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.