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PointNet

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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 +48 -45
  2. release_assets.json +4 -4
README.md CHANGED
@@ -14,7 +14,7 @@ pipeline_tag: image-segmentation
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  PointNet is a pioneering neural network architecture designed to directly consume unordered point cloud data for tasks such as classification and segmentation. It learns spatial features from raw 3D points without requiring voxelization or image projections.
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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/v0.57.3/src/qai_hub_models/models/pointnet) 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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  |---|---|---|---|---|
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- | 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/pointnet/releases/v0.57.3/pointnet-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/pointnet/releases/v0.57.3/pointnet-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/pointnet/releases/v0.57.3/pointnet-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[PointNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pointnet)**.
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  ### Option 2: Export with Custom Configurations
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- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/pointnet) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
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  - Custom input shapes
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  - Target device and runtime configurations
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44
  This option is ideal if you need to customize the model beyond the default configuration provided here.
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- See our repository for [PointNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.3/src/qai_hub_models/models/pointnet) for usage instructions.
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  ## Model Details
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@@ -57,45 +57,48 @@ See our repository for [PointNet 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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- | PointNet | ONNX | float | Snapdragon® X2 Elite | 0.305 ms | 213 - 213 MB | NPU
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- | PointNet | ONNX | float | Snapdragon® X Elite | 0.655 ms | 149 - 149 MB | NPU
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- | PointNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.406 ms | 59 - 91 MB | NPU
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- | PointNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 0.865 ms | 0 - 33 MB | NPU
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- | PointNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.647 ms | 0 - 12 MB | NPU
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- | PointNet | ONNX | float | Qualcomm® QCS8450 | 0.865 ms | 0 - 33 MB | NPU
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- | PointNet | ONNX | float | Snapdragon® 8 Elite Mobile | 0.421 ms | 0 - 24 MB | NPU
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- | PointNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.322 ms | 0 - 19 MB | NPU
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- | PointNet | ONNX | float | Qualcomm® QCS9075 | 0.807 ms | 0 - 52 MB | NPU
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- | PointNet | ONNX | float | Qualcomm® QCS8750 | 0.421 ms | 0 - 24 MB | NPU
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- | PointNet | ONNX | float | Qualcomm® QCS7181 | 0.655 ms | 149 - 149 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® X2 Elite | 0.403 ms | 0 - 0 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® X Elite | 0.771 ms | 0 - 0 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.41 ms | 0 - 42 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 0.865 ms | 0 - 47 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS8275 | 1.848 ms | 0 - 23 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.659 ms | 0 - 67 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS8450 | 0.865 ms | 0 - 47 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 0.485 ms | 0 - 23 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® SA8295P | 1.119 ms | 0 - 20 MB | NPU
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- | PointNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.323 ms | 0 - 23 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® SA7255P | 1.848 ms | 0 - 23 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS9075 | 0.799 ms | 0 - 2 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS8750 | 0.485 ms | 0 - 23 MB | NPU
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- | PointNet | QNN_DLC | float | Qualcomm® QCS7181 | 0.771 ms | 0 - 0 MB | NPU
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- | PointNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.41 ms | 0 - 40 MB | NPU
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- | PointNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 0.861 ms | 0 - 46 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® QCS8275 | 1.885 ms | 0 - 24 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.655 ms | 0 - 2 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® SA8775P | 4.276 ms | 0 - 16 MB | GPU
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- | PointNet | TFLITE | float | Qualcomm® SA8650P | 4.276 ms | 0 - 16 MB | GPU
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- | PointNet | TFLITE | float | Qualcomm® SA8255P | 4.276 ms | 0 - 16 MB | GPU
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- | PointNet | TFLITE | float | Qualcomm® QCS8450 | 0.861 ms | 0 - 46 MB | NPU
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- | PointNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 0.483 ms | 0 - 24 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® SA8295P | 1.122 ms | 0 - 21 MB | NPU
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- | PointNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.324 ms | 0 - 24 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® SA7255P | 1.885 ms | 0 - 24 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® QCS9075 | 0.801 ms | 0 - 8 MB | NPU
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- | PointNet | TFLITE | float | Qualcomm® QCS8750 | 0.483 ms | 0 - 24 MB | NPU
 
 
 
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  ## License
101
  * The license for the original implementation of PointNet can be found
 
14
 
15
  PointNet is a pioneering neural network architecture designed to directly consume unordered point cloud data for tasks such as classification and segmentation. It learns spatial features from raw 3D points without requiring voxelization or image projections.
16
 
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/pointnet) 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.
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  | 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/pointnet/releases/v0.58.0/pointnet-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/pointnet/releases/v0.58.0/pointnet-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/pointnet/releases/v0.58.0/pointnet-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[PointNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pointnet)**.
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/pointnet) 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 [PointNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/pointnet) for usage instructions.
47
 
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  ## Model Details
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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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+ | PointNet | ONNX | float | Snapdragon® X2 Elite | 0.303 ms | 1 - 1 MB | NPU
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+ | PointNet | ONNX | float | Snapdragon® X Elite | 0.656 ms | 7 - 7 MB | NPU
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+ | PointNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.399 ms | 0 - 43 MB | NPU
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+ | PointNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 0.869 ms | 0 - 48 MB | NPU
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+ | PointNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.651 ms | 0 - 2 MB | NPU
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+ | PointNet | ONNX | float | Qualcomm® QCS8450 | 0.869 ms | 0 - 48 MB | NPU
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+ | PointNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.321 ms | 0 - 23 MB | NPU
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+ | PointNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 0.807 ms | 0 - 52 MB | NPU
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+ | PointNet | ONNX | float | Snapdragon® 8 Elite Mobile | 0.418 ms | 0 - 23 MB | NPU
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+ | PointNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 0.418 ms | 0 - 23 MB | NPU
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+ | PointNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 0.656 ms | 7 - 7 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® X2 Elite | 0.412 ms | 0 - 0 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® X Elite | 0.777 ms | 1 - 1 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.416 ms | 0 - 42 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 0.868 ms | 0 - 47 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® QCS8275 | 1.903 ms | 0 - 23 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.667 ms | 0 - 1 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® SA8775P | 0.911 ms | 0 - 25 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® SA8650P | 0.911 ms | 0 - 25 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® SA8255P | 0.911 ms | 0 - 25 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® QCS8450 | 0.868 ms | 0 - 47 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.324 ms | 0 - 25 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 0.813 ms | 0 - 2 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® SA7255P | 1.903 ms | 0 - 23 MB | NPU
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+ | PointNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 0.484 ms | 0 - 22 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® SA8295P | 1.126 ms | 0 - 21 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 0.484 ms | 0 - 22 MB | NPU
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+ | PointNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 0.777 ms | 1 - 1 MB | NPU
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+ | PointNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.415 ms | 0 - 41 MB | NPU
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+ | PointNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 0.86 ms | 0 - 46 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® QCS8275 | 1.884 ms | 0 - 24 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 0.665 ms | 0 - 10 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® SA8775P | 0.924 ms | 0 - 25 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® SA8650P | 0.924 ms | 0 - 25 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® SA8255P | 0.924 ms | 0 - 25 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® QCS8450 | 0.86 ms | 0 - 46 MB | NPU
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+ | PointNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.323 ms | 0 - 25 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 0.801 ms | 0 - 8 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® SA7255P | 1.884 ms | 0 - 24 MB | NPU
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+ | PointNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 0.482 ms | 0 - 23 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® SA8295P | 1.129 ms | 0 - 21 MB | NPU
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+ | PointNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 0.482 ms | 0 - 23 MB | NPU
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  ## License
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  * The license for the original implementation of PointNet can be found
release_assets.json CHANGED
@@ -1,5 +1,5 @@
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  {
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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/pointnet/releases/v0.57.3/pointnet-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/pointnet/releases/v0.57.3/pointnet-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/pointnet/releases/v0.57.3/pointnet-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",
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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/pointnet/releases/v0.58.0/pointnet-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/pointnet/releases/v0.58.0/pointnet-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/pointnet/releases/v0.58.0/pointnet-onnx-float.zip"
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  }
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  }
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  }