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StereoNet

Depth Estimation
PyTorch
android
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v0.61.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.61.0 for changelog.

Files changed (2) hide show
  1. README.md +36 -36
  2. release_assets.json +4 -4
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
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  library_name: pytorch
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- license: other
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  tags:
5
  - android
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  pipeline_tag: depth-estimation
@@ -14,7 +14,7 @@ pipeline_tag: depth-estimation
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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).
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/v0.60.0/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
 
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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 |
29
  |---|---|---|---|---|
30
- | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.60.0/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/v0.60.0/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/v0.60.0/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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37
  ### Option 2: Export with Custom Configurations
38
 
39
- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/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/v0.60.0/src/qai_hub_models/models/stereonet) for usage instructions.
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  ## Model Details
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@@ -58,35 +58,35 @@ 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
60
  |---|---|---|---|---|---|---
61
- | StereoNet | ONNX | float | Snapdragon® X2 Elite | 208.109 ms | 5 - 5 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® X Elite | 377.314 ms | 44 - 44 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 299.963 ms | 6 - 4400 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 492.311 ms | 3 - 9 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 458.139 ms | 4 - 8 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 464.167 ms | 3 - 9 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 377.314 ms | 44 - 44 MB | NPU
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- | StereoNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 249.731 ms | 3 - 3236 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 249.731 ms | 3 - 3236 MB | NPU
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- | StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 198.933 ms | 3 - 3300 MB | NPU
71
- | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 191.479 ms | 3 - 3 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 363.646 ms | 3 - 3 MB | NPU
73
- | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 286.692 ms | 3 - 4451 MB | NPU
74
- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 482.377 ms | 3 - 10 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 1294.512 ms | 2 - 3260 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 463.723 ms | 3 - 7 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 462.148 ms | 1 - 3260 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA8650P | 462.148 ms | 1 - 3260 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA8255P | 462.148 ms | 1 - 3260 MB | NPU
80
- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 448.748 ms | 3 - 9 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 363.646 ms | 3 - 3 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 235.592 ms | 0 - 3243 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1294.512 ms | 2 - 3260 MB | NPU
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- | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 515.7 ms | 0 - 3366 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 235.592 ms | 0 - 3243 MB | NPU
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- | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 188.072 ms | 3 - 3302 MB | NPU
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- | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 302.03 ms | 73 - 3773 MB | NPU
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- | StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 302.03 ms | 73 - 3773 MB | NPU
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- | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 274.392 ms | 72 - 3863 MB | NPU
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91
  ## License
92
  * The license for the original implementation of StereoNet can be found
 
1
  ---
2
  library_name: pytorch
3
+ license: mit
4
  tags:
5
  - android
6
  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.
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/v0.61.0/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 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.61.0/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/v0.61.0/stereonet-qnn_dlc-float.zip)
32
+ | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.61.0/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/v0.61.0/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/v0.61.0/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 | 205.687 ms | 5 - 5 MB | NPU
62
+ | StereoNet | ONNX | float | Snapdragon® X Elite | 380.583 ms | 44 - 44 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 299.99 ms | 7 - 4398 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 491.744 ms | 3 - 9 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 428.175 ms | 0 - 49 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 534.614 ms | 3 - 9 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 380.583 ms | 44 - 44 MB | NPU
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+ | StereoNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 251.198 ms | 3 - 3234 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Elite Mobile | 251.198 ms | 3 - 3234 MB | NPU
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+ | StereoNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 202.336 ms | 3 - 3299 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® X2 Elite | 192.792 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® X Elite | 362.642 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 287.9 ms | 3 - 4450 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 483.015 ms | 3 - 10 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 1293.86 ms | 0 - 3260 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 435.821 ms | 3 - 7 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8775P | 462.152 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8650P | 462.152 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8255P | 462.152 ms | 2 - 3261 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 448.457 ms | 3 - 9 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 362.642 ms | 3 - 3 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 238.33 ms | 1 - 3242 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA7255P | 1293.86 ms | 0 - 3260 MB | NPU
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+ | StereoNet | QNN_DLC | float | Qualcomm® SA8295P | 516.045 ms | 0 - 3366 MB | NPU
85
+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 238.33 ms | 1 - 3242 MB | NPU
86
+ | StereoNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 187.034 ms | 3 - 3301 MB | NPU
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+ | StereoNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 279.882 ms | 51 - 3751 MB | NPU
88
+ | StereoNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 279.882 ms | 51 - 3751 MB | NPU
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+ | StereoNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 267.667 ms | 52 - 3842 MB | NPU
90
 
91
  ## License
92
  * 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": "0.60.0",
3
  "precisions": {
4
  "float": {
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  "universal_assets": {
@@ -8,19 +8,19 @@
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.27.1"
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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/v0.60.0/stereonet-onnx-float.zip"
12
  },
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  "qnn_dlc": {
14
  "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/v0.60.0/stereonet-qnn_dlc-float.zip"
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  },
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  "tflite": {
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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/v0.60.0/stereonet-tflite-float.zip"
24
  }
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  }
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  }
 
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  {
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+ "version": "0.61.0",
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  "precisions": {
4
  "float": {
5
  "universal_assets": {
 
8
  "qairt": "2.45.0.260326154327",
9
  "onnx_runtime": "1.27.1"
10
  },
11
+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/stereonet/releases/v0.61.0/stereonet-onnx-float.zip"
12
  },
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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/v0.61.0/stereonet-qnn_dlc-float.zip"
18
  },
19
  "tflite": {
20
  "tool_versions": {
21
  "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/v0.61.0/stereonet-tflite-float.zip"
24
  }
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  }
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  }