--- 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).