CenterNet-2D / README.md

v0.46.0

594c6f9 verified 10 days ago

9.35 kB

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

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/web-assets/model_demo.png)

	# CenterNet-2D: Optimized for Qualcomm Devices

	CenterNet-2D is machine learning model that detects objects by finding their center points.

	This is based on the implementation of CenterNet-2D found [here](https://github.com/xingyizhou/CenterNet).
	This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) 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 \|
	\|---\|---\|---\|---\|---\|
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® X Elite \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_x_elite.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8gen3.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_qcs8550_proxy.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_gen5.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS9075 \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_qcs9075.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® X Elite \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_x_elite.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Gen 3 Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8gen3.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8275 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8275_proxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8550 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8550_proxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8775P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa8775p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8_elite_gen5.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA7255P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa7255p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8295P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa8295p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS9075 \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs9075.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8450 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8450_proxy.zip)

	For more device-specific assets and performance metrics, visit [CenterNet-2D on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centernet_2d).


	### Option 2: Export with Custom Configurations

	Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) 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 [CenterNet-2D on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) for usage instructions.

	## Model Details

	Model Type: Model_use_case.object_detection

	Model Stats:
	- Model checkpoint: ctdet_coco_dla_2x.pth
	- Input resolution: 1 x 3 x 512 x 512
	- Number of parameters: 20.2M
	- Model size: 37.6 MB

	## Performance Summary
	\| Model \| Runtime \| Precision \| Chipset \| Inference Time (ms) \| Peak Memory Range (MB) \| Primary Compute Unit
	\|---\|---\|---\|---\|---\|---\|---
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® X Elite \| 382.325 ms \| 56 - 56 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 300.016 ms \| 20 - 32 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 396.256 ms \| 0 - 62 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS9075 \| 395.818 ms \| 9 - 14 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 290.376 ms \| 14 - 26 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 286.62 ms \| 17 - 28 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® X Elite \| 453.824 ms \| 3 - 3 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Gen 3 Mobile \| 302.668 ms \| 3 - 11 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8275 (Proxy) \| 571.069 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8550 (Proxy) \| 440.566 ms \| 4 - 5 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8775P \| 464.476 ms \| 2 - 11 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS9075 \| 455.061 ms \| 3 - 13 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8450 (Proxy) \| 646.934 ms \| 3 - 13 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA7255P \| 571.069 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8295P \| 498.197 ms \| 0 - 5 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 305.276 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 237.254 ms \| 3 - 13 MB \| NPU

	## License
	* The license for the original implementation of CenterNet-2D can be found
	[here](https://github.com/xingyizhou/CenterNet/blob/master/LICENSE).

	## References
	* [Objects as Points](https://arxiv.org/abs/1904.07850)
	* [Source Model Implementation](https://github.com/xingyizhou/CenterNet)

	## 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).

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

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/web-assets/model_demo.png)

	# CenterNet-2D: Optimized for Qualcomm Devices

	CenterNet-2D is machine learning model that detects objects by finding their center points.

	This is based on the implementation of CenterNet-2D found [here](https://github.com/xingyizhou/CenterNet).
	This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) 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 \|
	\|---\|---\|---\|---\|---\|
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® X Elite \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_x_elite.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8gen3.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_qcs8550_proxy.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_gen5.zip)
	\| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS9075 \| QAIRT 2.37, ONNX Runtime 1.23.0 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-precompiled_qnn_onnx-float-qualcomm_qcs9075.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® X Elite \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_x_elite.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Gen 3 Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8gen3.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8275 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8275_proxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8550 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8550_proxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8775P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa8775p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
	\| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_snapdragon_8_elite_gen5.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA7255P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa7255p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8295P \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_sa8295p.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS9075 \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs9075.zip)
	\| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8450 (Proxy) \| QAIRT 2.42 \| [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_2d/releases/v0.46.0/centernet_2d-qnn_context_binary-float-qualcomm_qcs8450_proxy.zip)

	For more device-specific assets and performance metrics, visit [CenterNet-2D on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centernet_2d).


	### Option 2: Export with Custom Configurations

	Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) 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 [CenterNet-2D on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_2d) for usage instructions.

	## Model Details

	Model Type: Model_use_case.object_detection

	Model Stats:
	- Model checkpoint: ctdet_coco_dla_2x.pth
	- Input resolution: 1 x 3 x 512 x 512
	- Number of parameters: 20.2M
	- Model size: 37.6 MB

	## Performance Summary
	\| Model \| Runtime \| Precision \| Chipset \| Inference Time (ms) \| Peak Memory Range (MB) \| Primary Compute Unit
	\|---\|---\|---\|---\|---\|---\|---
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® X Elite \| 382.325 ms \| 56 - 56 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Gen 3 Mobile \| 300.016 ms \| 20 - 32 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS8550 (Proxy) \| 396.256 ms \| 0 - 62 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Qualcomm® QCS9075 \| 395.818 ms \| 9 - 14 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 290.376 ms \| 14 - 26 MB \| NPU
	\| CenterNet-2D \| PRECOMPILED_QNN_ONNX \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 286.62 ms \| 17 - 28 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® X Elite \| 453.824 ms \| 3 - 3 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Gen 3 Mobile \| 302.668 ms \| 3 - 11 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8275 (Proxy) \| 571.069 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8550 (Proxy) \| 440.566 ms \| 4 - 5 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8775P \| 464.476 ms \| 2 - 11 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS9075 \| 455.061 ms \| 3 - 13 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® QCS8450 (Proxy) \| 646.934 ms \| 3 - 13 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA7255P \| 571.069 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Qualcomm® SA8295P \| 498.197 ms \| 0 - 5 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite For Galaxy Mobile \| 305.276 ms \| 0 - 9 MB \| NPU
	\| CenterNet-2D \| QNN_CONTEXT_BINARY \| float \| Snapdragon® 8 Elite Gen 5 Mobile \| 237.254 ms \| 3 - 13 MB \| NPU

	## License
	* The license for the original implementation of CenterNet-2D can be found
	[here](https://github.com/xingyizhou/CenterNet/blob/master/LICENSE).

	## References
	* [Objects as Points](https://arxiv.org/abs/1904.07850)
	* [Source Model Implementation](https://github.com/xingyizhou/CenterNet)

	## 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).