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library_name: pytorch
license: other
tags:
- android
pipeline_tag: depth-estimation
---
# StereoNet: Optimized for Qualcomm Devices
StereoNet is an end-to-end deep architecture for real-time stereo matching that produces high-quality, edge-preserved disparity maps from a rectified stereo image pair.
This is based on the implementation of StereoNet found [here](https://github.com/andrewlstewart/StereoNet_PyTorch).
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/v0.58.0/src/qai_hub_models/models/stereonet) 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 2.45, ONNX Runtime 1.25.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-onnx-float.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-tflite-float.zip)
For more device-specific assets and performance metrics, visit **[StereoNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/stereonet)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/stereonet) 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 [StereoNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/stereonet) for usage instructions.
## Model Details
**Model Type:** Model_use_case.depth_estimation
**Model Stats:**
- Model checkpoint: KeystoneDepth (epoch=21-step=696366.ckpt)
- Input resolution: 786x490
- Number of parameters: 1.94M
- Model size (float): 7.41 MB
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| StereoNet | ONNX | float | Snapdragon® X2 Elite | 206.313 ms | 5 - 5 MB | NPU
| StereoNet | ONNX | float | Snapdragon® X Elite | 375.632 ms | 44 - 44 MB | NPU
| StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 297.99 ms | 6 - 4393 MB | NPU
| StereoNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 444.646 ms | 0 - 49 MB | NPU
| StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 199.12 ms | 3 - 3301 MB | NPU
| StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 251.742 ms | 3 - 3235 MB | NPU
| StereoNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 251.742 ms | 3 - 3235 MB | NPU
| StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 375.632 ms | 44 - 44 MB | NPU
| StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 530.939 ms | 3 - 48 MB | NPU
| StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 193.3 ms | 3 - 3 MB | NPU
| StereoNet | QNN_DLC | float | Snapdragon® X Elite | 363.187 ms | 3 - 3 MB | NPU
| StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 285.647 ms | 3 - 4451 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® QCS8275 | 1294.016 ms | 1 - 3260 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 406.332 ms | 3 - 6 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 462.036 ms | 2 - 3261 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® SA8650P | 462.036 ms | 2 - 3261 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® SA8255P | 462.036 ms | 2 - 3261 MB | NPU
| StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 187.292 ms | 3 - 3301 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1294.016 ms | 1 - 3260 MB | NPU
| StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 237.606 ms | 1 - 3245 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 515.862 ms | 0 - 3367 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 237.606 ms | 1 - 3245 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 363.187 ms | 3 - 3 MB | NPU
| StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 511.428 ms | 5 - 11 MB | NPU
| StereoNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 401.979 ms | 72 - 5322 MB | NPU
| StereoNet | TFLITE | float | Qualcomm® SA8775P | 5701.173 ms | 2 - 33 MB | CPU
| StereoNet | TFLITE | float | Qualcomm® SA8650P | 5701.173 ms | 2 - 33 MB | CPU
| StereoNet | TFLITE | float | Qualcomm® SA8255P | 5701.173 ms | 2 - 33 MB | CPU
| StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 270.158 ms | 72 - 3865 MB | NPU
| StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 275.619 ms | 73 - 3773 MB | NPU
| StereoNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 275.619 ms | 73 - 3773 MB | NPU
| StereoNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 661.282 ms | 72 - 202 MB | NPU
## License
* The license for the original implementation of StereoNet can be found
[here](https://github.com/andrewlstewart/StereoNet_PyTorch/blob/main/LICENSE).
## References
* [StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction](https://arxiv.org/abs/1807.08865)
* [Source Model Implementation](https://github.com/andrewlstewart/StereoNet_PyTorch)
## 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).
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