README.md

---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: other

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/web-assets/model_demo.png)

# CenterPoint: Optimized for Qualcomm Devices

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.

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/main/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).

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.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.50.2/centerpoint-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.19.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centerpoint/releases/v0.50.2/centerpoint-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[CenterPoint on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centerpoint)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/centerpoint) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [CenterPoint on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/centerpoint) for usage instructions.

## Model Details

**Model Type:** Model_use_case.driver_assistance

**Model Stats:**
- Model checkpoint: PointPillars
- Input resolution: 5x20x5, 5x4, 5
- Number of parameters: 21.8M
- Model size: 83.3 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 170.399 ms | 2 - 444 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® X2 Elite | 181.941 ms | 2 - 2 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® X Elite | 311.167 ms | 2 - 2 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 243.528 ms | 0 - 752 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 909.695 ms | 0 - 451 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 318.018 ms | 2 - 1342 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS9075 | 396.904 ms | 2 - 11 MB | NPU
| CenterPoint | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 510.104 ms | 2 - 1069 MB | NPU
| CenterPoint | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 202.384 ms | 2 - 450 MB | NPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2576.773 ms | 1869 - 1880 MB | CPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4250.482 ms | 1837 - 1846 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 6207.515 ms | 1847 - 1855 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 4761.8 ms | 1869 - 1896 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS9075 | 5185.388 ms | 2363 - 2385 MB | CPU
| CenterPoint | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 5721.832 ms | 1838 - 1847 MB | CPU
| CenterPoint | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2758.856 ms | 1852 - 1865 MB | CPU

## License
* The license for the original implementation of CenterPoint can be found
  [here](https://github.com/tianweiy/CenterPoint/blob/master/LICENSE).



## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).