Depth-Anything: Optimized for Qualcomm Devices

Depth Anything is designed for estimating depth at each point in an image.

This is based on the implementation of Depth-Anything 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 1.24.3	Download
ONNX	w8a16	Universal	QAIRT 2.42, ONNX Runtime 1.24.3	Download
QNN_DLC	float	Universal	QAIRT 2.43	Download

For more device-specific assets and performance metrics, visit Depth-Anything 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 Depth-Anything on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.depth_estimation

Model Stats:

• Model checkpoint: DepthAnything_Small
• Input resolution: 518x518
• Number of parameters: 24.7M
• Model size (float): 94.3 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
Depth-Anything	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	22.355 ms	5 - 835 MB	NPU
Depth-Anything	ONNX	float	Snapdragon® X2 Elite	23.428 ms	51 - 51 MB	NPU
Depth-Anything	ONNX	float	Snapdragon® X Elite	56.096 ms	50 - 50 MB	NPU
Depth-Anything	ONNX	float	Snapdragon® 8 Gen 3 Mobile	37.792 ms	0 - 874 MB	NPU
Depth-Anything	ONNX	float	Qualcomm® QCS8550 (Proxy)	54.243 ms	0 - 61 MB	NPU
Depth-Anything	ONNX	float	Qualcomm® QCS9075	71.689 ms	3 - 9 MB	NPU
Depth-Anything	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	30.038 ms	2 - 648 MB	NPU
Depth-Anything	ONNX	w8a16	Snapdragon® 8 Elite Gen 5 Mobile	23.62 ms	15 - 783 MB	NPU
Depth-Anything	ONNX	w8a16	Snapdragon® X2 Elite	26.409 ms	34 - 34 MB	NPU
Depth-Anything	ONNX	w8a16	Snapdragon® X Elite	52.349 ms	32 - 32 MB	NPU
Depth-Anything	ONNX	w8a16	Snapdragon® 8 Gen 3 Mobile	36.834 ms	0 - 1014 MB	NPU
Depth-Anything	ONNX	w8a16	Qualcomm® QCS6490	3211.706 ms	150 - 156 MB	CPU
Depth-Anything	ONNX	w8a16	Qualcomm® QCS8550 (Proxy)	49.627 ms	12 - 17 MB	NPU
Depth-Anything	ONNX	w8a16	Qualcomm® QCS9075	74.383 ms	12 - 17 MB	NPU
Depth-Anything	ONNX	w8a16	Qualcomm® QCM6690	1665.551 ms	140 - 155 MB	CPU
Depth-Anything	ONNX	w8a16	Snapdragon® 8 Elite For Galaxy Mobile	30.558 ms	14 - 769 MB	NPU
Depth-Anything	ONNX	w8a16	Snapdragon® 7 Gen 4 Mobile	1611.546 ms	150 - 167 MB	CPU
Depth-Anything	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	21.223 ms	3 - 485 MB	NPU
Depth-Anything	QNN_DLC	float	Snapdragon® X2 Elite	22.232 ms	3 - 3 MB	NPU
Depth-Anything	QNN_DLC	float	Snapdragon® X Elite	55.931 ms	3 - 3 MB	NPU
Depth-Anything	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	38.478 ms	0 - 609 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	144.042 ms	1 - 466 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	53.468 ms	3 - 535 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® SA8775P	59.426 ms	1 - 465 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® QCS9075	75.648 ms	3 - 9 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	90.708 ms	2 - 563 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® SA7255P	144.042 ms	1 - 466 MB	NPU
Depth-Anything	QNN_DLC	float	Qualcomm® SA8295P	80.664 ms	0 - 456 MB	NPU
Depth-Anything	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	26.804 ms	0 - 468 MB	NPU

License

• The license for the original implementation of Depth-Anything can be found here.

References

• Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
• 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.