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CenterPoint

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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 +36 -36
  2. release_assets.json +6 -8
README.md CHANGED
@@ -13,7 +13,7 @@ pipeline_tag: other
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  CenterPoint is a LiDAR-based 3D object detection model that detects objects by predicting their centers and regressing other attributes. It is designed for high accuracy and real-time performance in autonomous driving applications.
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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/centerpoint) 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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@@ -26,22 +26,22 @@ 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/centerpoint/releases/v0.58.0/centerpoint-qnn_dlc-float.zip)
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- | TFLITE | float | Universal | | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.58.0/centerpoint-tflite-float.zip)
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  For more device-specific assets and performance metrics, visit **[CenterPoint on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centerpoint)**.
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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/v0.58.0/src/qai_hub_models/models/centerpoint) 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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  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 [CenterPoint on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/centerpoint) for usage instructions.
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  ## Model Details
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@@ -56,37 +56,37 @@ See our repository for [CenterPoint on GitHub](https://github.com/qualcomm/ai-hu
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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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- | CenterPoint | QNN_DLC | float | Snapdragon® X2 Elite | 185.299 ms | 2 - 2 MB | NPU
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- | CenterPoint | QNN_DLC | float | Snapdragon® X Elite | 322.405 ms | 2 - 2 MB | NPU
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- | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 253.291 ms | 0 - 749 MB | NPU
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- | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 533.942 ms | 2 - 737 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® QCS8275 | 920.091 ms | 1 - 450 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 336.27 ms | 2 - 4 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® SA8775P | 397.217 ms | 1 - 703 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® SA8650P | 397.217 ms | 1 - 703 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® SA8255P | 397.217 ms | 1 - 703 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® QCS8450 | 533.942 ms | 2 - 737 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 388.031 ms | 2 - 11 MB | NPU
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- | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 175.303 ms | 2 - 722 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® SA7255P | 920.091 ms | 1 - 450 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® SA8295P | 443.469 ms | 1 - 449 MB | NPU
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- | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 210.364 ms | 0 - 462 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 210.364 ms | 0 - 462 MB | NPU
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- | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 322.405 ms | 2 - 2 MB | NPU
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- | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4098.792 ms | 1851 - 1860 MB | CPU
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- | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5627.607 ms | 1837 - 1846 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® QCS8275 | 6205.521 ms | 1849 - 1856 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5014.262 ms | 1837 - 1838 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® SA8775P | 5407.216 ms | 1810 - 1815 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® SA8650P | 5407.216 ms | 1810 - 1815 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® SA8255P | 5407.216 ms | 1810 - 1815 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® QCS8450 | 5627.607 ms | 1837 - 1846 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5147.069 ms | 2364 - 2385 MB | CPU
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- | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 839.133 ms | 2003 - 2012 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® SA7255P | 6205.521 ms | 1849 - 1856 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® SA8295P | 3450.647 ms | 1810 - 1816 MB | CPU
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- | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Mobile | 2808.061 ms | 1806 - 1814 MB | CPU
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- | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2808.061 ms | 1806 - 1814 MB | CPU
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  ## License
92
  * The license for the original implementation of CenterPoint can be found
 
13
 
14
  CenterPoint is a LiDAR-based 3D object detection model that detects objects by predicting their centers and regressing other attributes. It is designed for high accuracy and real-time performance in autonomous driving applications.
15
 
16
+ 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/centerpoint) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
17
 
18
  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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26
 
27
  | Runtime | Precision | Chipset | SDK Versions | Download |
28
  |---|---|---|---|---|
29
+ | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.59.0/centerpoint-qnn_dlc-float.zip)
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+ | TFLITE | float | Universal | | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.59.0/centerpoint-tflite-float.zip)
31
 
32
  For more device-specific assets and performance metrics, visit **[CenterPoint on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centerpoint)**.
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/centerpoint) 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 [CenterPoint on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/centerpoint) for usage instructions.
45
 
46
  ## Model Details
47
 
 
56
  ## Performance Summary
57
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
58
  |---|---|---|---|---|---|---
59
+ | CenterPoint | QNN_DLC | float | Snapdragon® X2 Elite | 185.015 ms | 2 - 2 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Snapdragon® X Elite | 326.336 ms | 2 - 2 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 249.841 ms | 0 - 750 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 521.899 ms | 2 - 737 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 920.314 ms | 1 - 450 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 332.766 ms | 2 - 6 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® SA8775P | 397.325 ms | 1 - 703 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® SA8650P | 397.325 ms | 1 - 703 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® SA8255P | 397.325 ms | 1 - 703 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® QCS8450 | 521.899 ms | 2 - 737 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 388.854 ms | 4 - 13 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 326.336 ms | 2 - 2 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 207.78 ms | 0 - 461 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® SA7255P | 920.314 ms | 1 - 450 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Qualcomm® SA8295P | 442.028 ms | 1 - 449 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 207.78 ms | 0 - 461 MB | NPU
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+ | CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 175.341 ms | 2 - 723 MB | NPU
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+ | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4086.509 ms | 1835 - 1844 MB | CPU
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+ | CenterPoint | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5376.29 ms | 1860 - 1876 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ QCS8275 | 6484.61 ms | 1847 - 1856 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5056.352 ms | 1893 - 1894 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® SA8775P | 5375.999 ms | 1810 - 1816 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® SA8650P | 5375.999 ms | 1810 - 1816 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® SA8255P | 5375.999 ms | 1810 - 1816 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® QCS8450 | 5376.29 ms | 1860 - 1876 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5153.011 ms | 2363 - 2385 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2647.479 ms | 1853 - 1865 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® SA7255P | 6484.61 ms | 1847 - 1856 MB | CPU
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+ | CenterPoint | TFLITE | float | Qualcomm® SA8295P | 3446.214 ms | 1808 - 1814 MB | CPU
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+ | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Mobile | 2647.479 ms | 1853 - 1865 MB | CPU
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+ | CenterPoint | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 803.09 ms | 1981 - 1991 MB | CPU
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  ## License
92
  * The license for the original implementation of CenterPoint can be found
release_assets.json CHANGED
@@ -1,19 +1,17 @@
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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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- "tflite": {
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- "tool_versions": {
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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/centerpoint/releases/v0.58.0/centerpoint-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/centerpoint/releases/v0.58.0/centerpoint-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": {
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  "float": {
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  "universal_assets": {
 
 
 
 
 
 
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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/centerpoint/releases/v0.59.0/centerpoint-qnn_dlc-float.zip"
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+ },
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+ "tflite": {
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+ "tool_versions": {},
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.59.0/centerpoint-tflite-float.zip"
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  }
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  }
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  }