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StereoNet

Depth Estimation
PyTorch
android
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v0.58.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.58.0 for changelog.

Files changed (2) hide show
  1. README.md +35 -31
  2. release_assets.json +4 -4
README.md CHANGED
@@ -14,7 +14,7 @@ pipeline_tag: depth-estimation
14
  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).
17
- 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/v0.57.3/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.
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@@ -27,23 +27,23 @@ Below are pre-exported model assets ready for deployment.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
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  |---|---|---|---|---|
30
- | 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/v0.57.3/stereonet-onnx-float.zip)
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- | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.57.3/stereonet-qnn_dlc-float.zip)
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- | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.57.3/stereonet-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[StereoNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/stereonet)**.
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  ### Option 2: Export with Custom Configurations
38
 
39
- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/stereonet) Python library to compile and export the model with your own:
40
  - Custom weights (e.g., fine-tuned checkpoints)
41
  - Custom input shapes
42
  - Target device and runtime configurations
43
 
44
  This option is ideal if you need to customize the model beyond the default configuration provided here.
45
 
46
- See our repository for [StereoNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/stereonet) for usage instructions.
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  ## Model Details
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@@ -58,34 +58,38 @@ See our repository for [StereoNet on GitHub](https://github.com/qualcomm/ai-hub-
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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  |---|---|---|---|---|---|---
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- | StereoNet | ONNX | float | Snapdragon® X2 Elite | 206.031 ms | 211 - 211 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® X Elite | 379.645 ms | 148 - 148 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 299.865 ms | 6 - 4386 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 385.442 ms | 0 - 49 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 250.085 ms | 3 - 3239 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 198.913 ms | 3 - 3295 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® QCS9075 | 530.939 ms | 3 - 48 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® QCS8750 | 250.085 ms | 3 - 3239 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® QCS7181 | 379.645 ms | 148 - 148 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 193.151 ms | 3 - 3 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 366.078 ms | 3 - 3 MB | NPU
72
- | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 285.954 ms | 3 - 4452 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® QCS8275 | 1294.103 ms | 3 - 3262 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 444.187 ms | 3 - 7 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 238.254 ms | 0 - 3240 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 515.727 ms | 0 - 3366 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 186.378 ms | 3 - 3303 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® QCS9075 | 511.428 ms | 5 - 11 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1294.103 ms | 3 - 3262 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® QCS8750 | 238.254 ms | 0 - 3240 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® QCS7181 | 366.078 ms | 3 - 3 MB | NPU
 
 
 
 
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  | StereoNet | TFLITE | float | Qualcomm® SA8775P | 5701.173 ms | 2 - 33 MB | CPU
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  | StereoNet | TFLITE | float | Qualcomm® SA8650P | 5701.173 ms | 2 - 33 MB | CPU
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  | StereoNet | TFLITE | float | Qualcomm® SA8255P | 5701.173 ms | 2 - 33 MB | CPU
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- | StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 280.229 ms | 74 - 3775 MB | NPU
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- | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 275.317 ms | 73 - 3857 MB | NPU
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- | StereoNet | TFLITE | float | Qualcomm® QCS9075 | 661.282 ms | 72 - 202 MB | NPU
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- | StereoNet | TFLITE | float | Qualcomm® QCS8750 | 280.229 ms | 74 - 3775 MB | NPU
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90
  ## License
91
  * The license for the original implementation of StereoNet can be found
 
14
  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.
15
 
16
  This is based on the implementation of StereoNet found [here](https://github.com/andrewlstewart/StereoNet_PyTorch).
17
+ 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).
18
 
19
  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.
20
 
 
27
 
28
  | Runtime | Precision | Chipset | SDK Versions | Download |
29
  |---|---|---|---|---|
30
+ | 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)
31
+ | 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)
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+ | 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)
33
 
34
  For more device-specific assets and performance metrics, visit **[StereoNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/stereonet)**.
35
 
36
 
37
  ### Option 2: Export with Custom Configurations
38
 
39
+ 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:
40
  - Custom weights (e.g., fine-tuned checkpoints)
41
  - Custom input shapes
42
  - Target device and runtime configurations
43
 
44
  This option is ideal if you need to customize the model beyond the default configuration provided here.
45
 
46
+ 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.
47
 
48
  ## Model Details
49
 
 
58
  ## Performance Summary
59
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
60
  |---|---|---|---|---|---|---
61
+ | StereoNet | ONNX | float | Snapdragon® X2 Elite | 206.313 ms | 5 - 5 MB | NPU
62
+ | StereoNet | ONNX | float | Snapdragon® X Elite | 375.632 ms | 44 - 44 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 297.99 ms | 6 - 4393 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 444.646 ms | 0 - 49 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 199.12 ms | 3 - 3301 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 251.742 ms | 3 - 3235 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 251.742 ms | 3 - 3235 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 375.632 ms | 44 - 44 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 530.939 ms | 3 - 48 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 193.3 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 363.187 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 285.647 ms | 3 - 4451 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® QCS8275 | 1294.016 ms | 1 - 3260 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 406.332 ms | 3 - 6 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 462.036 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8650P | 462.036 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8255P | 462.036 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 187.292 ms | 3 - 3301 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1294.016 ms | 1 - 3260 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 237.606 ms | 1 - 3245 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 515.862 ms | 0 - 3367 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 237.606 ms | 1 - 3245 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 363.187 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 511.428 ms | 5 - 11 MB | NPU
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+ | StereoNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 401.979 ms | 72 - 5322 MB | NPU
86
  | StereoNet | TFLITE | float | Qualcomm® SA8775P | 5701.173 ms | 2 - 33 MB | CPU
87
  | StereoNet | TFLITE | float | Qualcomm® SA8650P | 5701.173 ms | 2 - 33 MB | CPU
88
  | StereoNet | TFLITE | float | Qualcomm® SA8255P | 5701.173 ms | 2 - 33 MB | CPU
89
+ | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 270.158 ms | 72 - 3865 MB | NPU
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+ | StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 275.619 ms | 73 - 3773 MB | NPU
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+ | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 275.619 ms | 73 - 3773 MB | NPU
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+ | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 661.282 ms | 72 - 202 MB | NPU
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94
  ## License
95
  * The license for the original implementation of StereoNet can be found
release_assets.json CHANGED
@@ -1,5 +1,5 @@
1
  {
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- "version": "0.57.3",
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  "precisions": {
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  "float": {
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  "universal_assets": {
@@ -8,20 +8,20 @@
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  "qairt": "2.45.0.260326154327",
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  "litert": "1.4.4"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.57.3/stereonet-tflite-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.57.3/stereonet-qnn_dlc-float.zip"
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  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.25.0"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.57.3/stereonet-onnx-float.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.58.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
 
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  "qairt": "2.45.0.260326154327",
9
  "litert": "1.4.4"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-tflite-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-qnn_dlc-float.zip"
18
  },
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  "onnx": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.25.0"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.58.0/stereonet-onnx-float.zip"
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  }
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  }
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  }