--- title: StereoNet canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/StereoNet" md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/StereoNet.md" repository: Qualcomm-AI-Hub/StereoNet last_updated: 2026-10-16 license: mit downloads: 0 stars: 0 tags: - android --- # StereoNet > StereoNet - Qualcomm-AI-Hub 在 ModelScope 开源的模型。StereoNet: Optimized for Qualcomm Devices Qualcomm-AI-Hub/StereoNet 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可。 - **Repository**: Qualcomm-AI-Hub/StereoNet - **License**: mit - **Tags**: android - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/StereoNet --- ![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/stereonet/web-assets/model_demo.png) # 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-cn/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). Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.cn) 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 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/stereonet/releases/v0.62.1/stereonet-onnx-float.zip) | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/stereonet/releases/v0.62.1/stereonet-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/stereonet/releases/v0.62.1/stereonet-tflite-float.zip) For more device-specific assets and performance metrics, visit **[StereoNet on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/stereonet)**. ### Option 2: Export with Custom Configurations Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/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-cn/blob/main/src/qai_hub_models/models/stereonet) for usage instructions. ## Model Details **Model Type:** Model_use_case.depth_estimation **Model Stats:** - Input resolution: 786x490 - Model checkpoint: KeystoneDepth (epoch=21-step=696366.ckpt) - Model size (float): 7.41 MB - Number of parameters: 1.94M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | StereoNet | ONNX | float | Snapdragon® X2 Elite | 207.049 ms | 5 - 5 MB | NPU | StereoNet | ONNX | float | Snapdragon® X Elite | 378.219 ms | 44 - 44 MB | NPU | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 296.95 ms | 6 - 4395 MB | NPU | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 495.508 ms | 3 - 9 MB | NPU | StereoNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 429.401 ms | 3 - 52 MB | NPU | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 464.01 ms | 3 - 9 MB | NPU | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 378.219 ms | 44 - 44 MB | NPU | StereoNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 250.438 ms | 3 - 3236 MB | NPU | StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 250.438 ms | 3 - 3236 MB | NPU | StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 199.162 ms | 3 - 3303 MB | NPU | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 193.328 ms | 3 - 3 MB | NPU | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 365.661 ms | 3 - 3 MB | NPU | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 285.047 ms | 3 - 4451 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 482.907 ms | 3 - 10 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 473.14 ms | 3 - 8 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 461.966 ms | 1 - 3260 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® SA8650P | 461.966 ms | 1 - 3260 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® SA8255P | 461.966 ms | 1 - 3260 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 450.574 ms | 3 - 9 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 365.661 ms | 3 - 3 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 237.798 ms | 0 - 3238 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1293.634 ms | 1 - 3259 MB | NPU | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 516.242 ms | 0 - 3366 MB | NPU | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 237.798 ms | 0 - 3238 MB | NPU | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 187.254 ms | 3 - 3301 MB | NPU | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 551.746 ms | 74 - 78 MB | NPU | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 276.155 ms | 73 - 3773 MB | NPU | StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 276.155 ms | 73 - 3773 MB | NPU | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 288.841 ms | 74 - 3864 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 * For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).