README.md

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

---

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

# 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](https://github.com/HuangJunJie2017/BEVDet/).
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/bevdet) 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 |
|---|---|---|---|---|
| ONNX | float | Universal | ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/bevdet/releases/v0.46.0/bevdet-onnx-float.zip)
| ONNX | w8a16_mixed_fp16 | Universal | ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/bevdet/releases/v0.46.0/bevdet-onnx-w8a16_mixed_fp16.zip)
| TFLITE | float | Universal | TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/bevdet/releases/v0.46.0/bevdet-tflite-float.zip)

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


### 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/bevdet) 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](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/bevdet) 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® X Elite | 680.081 ms | 600 - 600 MB | CPU
| BEVDet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 2323.529 ms | 278 - 288 MB | CPU
| BEVDet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2565.452 ms | 274 - 281 MB | CPU
| BEVDet | ONNX | float | Qualcomm® QCS9075 | 1536.227 ms | 304 - 320 MB | CPU
| BEVDet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1625.923 ms | 275 - 287 MB | CPU
| BEVDet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1681.251 ms | 299 - 310 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Snapdragon® X Elite | 805.09 ms | 1105 - 1105 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 2317.188 ms | 258 - 273 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | 2662.588 ms | 311 - 325 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Qualcomm® QCS9075 | 1750.335 ms | 338 - 354 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite For Galaxy Mobile | 1711.346 ms | 319 - 329 MB | CPU
| BEVDet | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 1831.786 ms | 314 - 327 MB | CPU
| BEVDet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1670.163 ms | 123 - 139 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 3149.836 ms | 129 - 139 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 1927.239 ms | 103 - 105 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® SA8775P | 2522.77 ms | 128 - 139 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® QCS9075 | 2425.619 ms | 126 - 1473 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 2671.358 ms | 129 - 149 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® SA7255P | 3149.836 ms | 129 - 139 MB | CPU
| BEVDet | TFLITE | float | Qualcomm® SA8295P | 2008.213 ms | 87 - 95 MB | CPU
| BEVDet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1256.893 ms | 75 - 85 MB | CPU
| BEVDet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1069.57 ms | 89 - 100 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](https://arxiv.org/abs/2112.11790)
* [Source Model Implementation](https://github.com/HuangJunJie2017/BEVDet/)

## 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).