BEVDet: Optimized for Qualcomm Devices

BEVDet is a machine learning model for generating a birds eye view represenation from the sensors(cameras) mounted on a vehicle.

This is based on the implementation of BEVDet found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up 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
ONNX	float	Universal	ONNX Runtime 1.24.3	Download
ONNX	w8a16_mixed_fp16	Universal	ONNX Runtime 1.24.3	Download
TFLITE	float	Universal		Download

For more device-specific assets and performance metrics, visit BEVDet on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models 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 BEVDet on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.driver_assistance

Model Stats:

• Model checkpoint: bevdet-r50.pth
• Input resolution: 1 x 6 x 3 x 256 x 704
• Number of parameters: 44M
• Model size: 171 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
BEVDet	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	1326.889 ms	252 - 263 MB	CPU
BEVDet	ONNX	float	Snapdragon® X2 Elite	606.835 ms	736 - 736 MB	CPU
BEVDet	ONNX	float	Snapdragon® X Elite	621.617 ms	732 - 732 MB	CPU
BEVDet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	2090.336 ms	216 - 226 MB	CPU
BEVDet	ONNX	float	Qualcomm® QCS8550 (Proxy)	2652.368 ms	187 - 189 MB	CPU
BEVDet	ONNX	float	Qualcomm® QCS9075	1523.403 ms	236 - 250 MB	CPU
BEVDet	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	1381.605 ms	248 - 262 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Snapdragon® 8 Elite Gen 5 Mobile	1565.626 ms	319 - 333 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Snapdragon® X2 Elite	905.903 ms	708 - 708 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Snapdragon® X Elite	889.641 ms	1239 - 1239 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Snapdragon® 8 Gen 3 Mobile	2280.19 ms	367 - 378 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Qualcomm® QCS8550 (Proxy)	2676.944 ms	398 - 401 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Qualcomm® QCS9075	1855.02 ms	425 - 435 MB	CPU
BEVDet	ONNX	w8a16_mixed_fp16	Snapdragon® 8 Elite For Galaxy Mobile	1535.849 ms	329 - 343 MB	CPU
BEVDet	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	1173.851 ms	87 - 97 MB	CPU
BEVDet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	1836.121 ms	106 - 118 MB	CPU
BEVDet	TFLITE	float	Qualcomm® QCS8275 (Proxy)	3146.339 ms	128 - 136 MB	CPU
BEVDet	TFLITE	float	Qualcomm® QCS8550 (Proxy)	1999.129 ms	128 - 131 MB	CPU
BEVDet	TFLITE	float	Qualcomm® SA8775P	2489.569 ms	127 - 138 MB	CPU
BEVDet	TFLITE	float	Qualcomm® QCS9075	2391.956 ms	126 - 1330 MB	CPU
BEVDet	TFLITE	float	Qualcomm® QCS8450 (Proxy)	2625.93 ms	122 - 139 MB	CPU
BEVDet	TFLITE	float	Qualcomm® SA7255P	3146.339 ms	128 - 136 MB	CPU
BEVDet	TFLITE	float	Qualcomm® SA8295P	1789.726 ms	127 - 137 MB	CPU
BEVDet	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	1275.134 ms	108 - 121 MB	CPU

License

• The license for the original implementation of BEVDet can be found [here](https://github.com/HuangJunJie2017/BEVDet/blob/dev3.0/LICENSE https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf).

References

• BEVDet: High-Performance Multi-Camera 3D Object Detection in Bird-Eye-View
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.