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MaskRCNN

Image Segmentation
PyTorch
android
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v0.59.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.59.0 for changelog.

Files changed (2) hide show
  1. README.md +38 -32
  2. release_assets.json +2 -2
README.md CHANGED
@@ -14,7 +14,7 @@ pipeline_tag: image-segmentation
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  Mask R-CNN is a machine learning model that extends Faster R-CNN to perform instance segmentation by detecting objects in an image while simultaneously generating a high-quality segmentation mask for each instance. It adds a branch for predicting segmentation masks in parallel with the existing branch for bounding box recognition.
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  This is based on the implementation of MaskRCNN found [here](https://github.com/pytorch/vision).
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- 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/maskrcnn) 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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@@ -27,21 +27,21 @@ Below are pre-exported model assets ready for deployment.
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  | Runtime | Precision | Chipset | SDK Versions | Download |
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  |---|---|---|---|---|
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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/maskrcnn/releases/v0.58.0/maskrcnn-qnn_dlc-float.zip)
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  For more device-specific assets and performance metrics, visit **[MaskRCNN on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/maskrcnn)**.
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  ### Option 2: Export with Custom Configurations
36
 
37
- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/maskrcnn) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
39
  - Custom input shapes
40
  - Target device and runtime configurations
41
 
42
  This option is ideal if you need to customize the model beyond the default configuration provided here.
43
 
44
- See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/maskrcnn) for usage instructions.
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  ## Model Details
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@@ -57,34 +57,40 @@ See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-m
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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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- | proposal_generator | QNN_DLC | float | Snapdragon® X2 Elite | 42.855 ms | 7 - 7 MB | NPU
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- | proposal_generator | QNN_DLC | float | Snapdragon® X Elite | 91.325 ms | 7 - 7 MB | NPU
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- | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 70.051 ms | 7 - 2327 MB | NPU
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- | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 152.938 ms | 8 - 2736 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® QCS8275 | 354.153 ms | 2 - 1758 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 95.709 ms | 7 - 16 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® QCS8450 | 152.938 ms | 8 - 2736 MB | NPU
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- | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 53.463 ms | 0 - 1510 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® SA8295P | 125.945 ms | 3 - 1432 MB | NPU
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- | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 41.81 ms | 7 - 1789 MB | NPU
70
- | proposal_generator | QNN_DLC | float | Qualcomm® SA7255P | 354.153 ms | 2 - 1758 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 118.978 ms | 7 - 71 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 53.463 ms | 0 - 1510 MB | NPU
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- | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 91.325 ms | 7 - 7 MB | NPU
74
- | roi_head | QNN_DLC | float | Snapdragon® X2 Elite | 98.085 ms | 52 - 52 MB | NPU
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- | roi_head | QNN_DLC | float | Snapdragon® X Elite | 250.621 ms | 52 - 52 MB | NPU
76
- | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 177.887 ms | 13 - 860 MB | NPU
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- | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 312.774 ms | 39 - 960 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® QCS8275 | 573.715 ms | 49 - 744 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 242.438 ms | 52 - 54 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® QCS8450 | 312.774 ms | 39 - 960 MB | NPU
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- | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 129.478 ms | 39 - 730 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® SA8295P | 325.17 ms | 49 - 846 MB | NPU
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- | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 96.26 ms | 15 - 721 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® SA7255P | 573.715 ms | 49 - 744 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 324.226 ms | 52 - 106 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 129.478 ms | 39 - 730 MB | NPU
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- | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 250.621 ms | 52 - 52 MB | NPU
 
 
 
 
 
 
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  ## License
90
  * The license for the original implementation of MaskRCNN can be found
 
14
  Mask R-CNN is a machine learning model that extends Faster R-CNN to perform instance segmentation by detecting objects in an image while simultaneously generating a high-quality segmentation mask for each instance. It adds a branch for predicting segmentation masks in parallel with the existing branch for bounding box recognition.
15
 
16
  This is based on the implementation of MaskRCNN found [here](https://github.com/pytorch/vision).
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/v0.59.0/src/qai_hub_models/models/maskrcnn) 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
+ | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/maskrcnn/releases/v0.59.0/maskrcnn-qnn_dlc-float.zip)
31
 
32
  For more device-specific assets and performance metrics, visit **[MaskRCNN on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/maskrcnn)**.
33
 
34
 
35
  ### Option 2: Export with Custom Configurations
36
 
37
+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/maskrcnn) Python library to compile and export the model with your own:
38
  - Custom weights (e.g., fine-tuned checkpoints)
39
  - Custom input shapes
40
  - Target device and runtime configurations
41
 
42
  This option is ideal if you need to customize the model beyond the default configuration provided here.
43
 
44
+ See our repository for [MaskRCNN on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/maskrcnn) for usage instructions.
45
 
46
  ## Model Details
47
 
 
57
  ## Performance Summary
58
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
59
  |---|---|---|---|---|---|---
60
+ | proposal_generator | QNN_DLC | float | Snapdragon® X2 Elite | 42.554 ms | 7 - 7 MB | NPU
61
+ | proposal_generator | QNN_DLC | float | Snapdragon® X Elite | 91.369 ms | 7 - 7 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 70.212 ms | 7 - 2326 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 151.831 ms | 7 - 2736 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 354.445 ms | 2 - 1759 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 94.473 ms | 7 - 24 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® SA8775P | 122.283 ms | 2 - 1759 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® SA8650P | 122.283 ms | 2 - 1759 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® SA8255P | 122.283 ms | 2 - 1759 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® QCS8450 | 151.831 ms | 7 - 2736 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 122.578 ms | 7 - 71 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 91.369 ms | 7 - 7 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 53.816 ms | 12 - 1523 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® SA7255P | 354.445 ms | 2 - 1759 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Qualcomm® SA8295P | 125.987 ms | 0 - 1430 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 53.816 ms | 12 - 1523 MB | NPU
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+ | proposal_generator | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 42.27 ms | 7 - 1789 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® X2 Elite | 98.312 ms | 52 - 52 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® X Elite | 257.611 ms | 52 - 52 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 177.662 ms | 49 - 897 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 300.661 ms | 0 - 918 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 573.44 ms | 30 - 725 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 243.427 ms | 52 - 679 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® SA8775P | 270.285 ms | 49 - 925 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® SA8650P | 270.285 ms | 49 - 925 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® SA8255P | 270.285 ms | 49 - 925 MB | NPU
86
+ | roi_head | QNN_DLC | float | Qualcomm® QCS8450 | 300.661 ms | 0 - 918 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 279.963 ms | 52 - 106 MB | NPU
88
+ | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 257.611 ms | 52 - 52 MB | NPU
89
+ | roi_head | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 125.217 ms | 34 - 726 MB | NPU
90
+ | roi_head | QNN_DLC | float | Qualcomm® SA7255P | 573.44 ms | 30 - 725 MB | NPU
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+ | roi_head | QNN_DLC | float | Qualcomm® SA8295P | 306.965 ms | 49 - 847 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 125.217 ms | 34 - 726 MB | NPU
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+ | roi_head | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 92.646 ms | 13 - 719 MB | NPU
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95
  ## License
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  * The license for the original implementation of MaskRCNN can be found
release_assets.json CHANGED
@@ -1,5 +1,5 @@
1
  {
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- "version": "0.58.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
@@ -7,7 +7,7 @@
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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/maskrcnn/releases/v0.58.0/maskrcnn-qnn_dlc-float.zip"
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  }
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  }
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  }
 
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  {
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+ "version": "0.59.0",
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  "precisions": {
4
  "float": {
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  "universal_assets": {
 
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  "tool_versions": {
8
  "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/maskrcnn/releases/v0.59.0/maskrcnn-qnn_dlc-float.zip"
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  }
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  }
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  }