| --- |
| library_name: pytorch |
| license: other |
| tags: |
| - android |
| pipeline_tag: depth-estimation |
|
|
| --- |
| |
|  |
|
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| # StereoNet: Optimized for Qualcomm Devices |
|
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| 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. |
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| 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/main/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). |
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| 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 |
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| Below are pre-exported model assets ready for deployment. |
|
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| | 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/stereonet/releases/v0.51.0/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/v0.51.0/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/v0.51.0/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/main/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/main/src/qai_hub_models/models/stereonet) for usage instructions. |
| |
| ## Model Details |
| |
| **Model Type:** Model_use_case.depth_estimation |
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| **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® 8 Elite Gen 5 Mobile | 196.394 ms | 6 - 1359 MB | NPU |
| | StereoNet | ONNX | float | Snapdragon® X2 Elite | 180.442 ms | 20 - 20 MB | NPU |
| | StereoNet | ONNX | float | Snapdragon® X Elite | 330.195 ms | 19 - 19 MB | NPU |
| | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 261.045 ms | 6 - 1978 MB | NPU |
| | StereoNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 353.067 ms | 0 - 24 MB | NPU |
| | StereoNet | ONNX | float | Qualcomm® QCS9075 | 513.168 ms | 3 - 6 MB | NPU |
| | StereoNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 219.781 ms | 3 - 1324 MB | NPU |
| | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 188.608 ms | 3 - 3263 MB | NPU |
| | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 194.982 ms | 3 - 3 MB | NPU |
| | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 362.226 ms | 3 - 3 MB | NPU |
| | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 310.814 ms | 3 - 4452 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1293.97 ms | 1 - 3260 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 433.689 ms | 3 - 1112 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 461.879 ms | 0 - 3260 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® QCS9075 | 510.602 ms | 3 - 9 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1293.97 ms | 1 - 3260 MB | NPU |
| | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 515.878 ms | 0 - 3366 MB | NPU |
| | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 237.626 ms | 2 - 3245 MB | NPU |
| | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 257.68 ms | 72 - 3823 MB | NPU |
| | StereoNet | TFLITE | float | Qualcomm® QCS9075 | 661.686 ms | 72 - 202 MB | NPU |
| | StereoNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 277.134 ms | 73 - 3773 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). |
| |
