| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - bev |
| --- |
| |
| # BEV (ResNet-50) |
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| This model follows the LSS (Lift-Splat-Shoot) view transformation approach: 6 camera images are processed by ResNet-50 + FPN for multi-scale features, projected to the BEV plane via LSSTransformer, encoded by BevEncoder, and finally predicted on the BEV grid by CenterPointHead for 3D bounding boxes. |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | BEV-VTv2 | 6-camera multi-view images `(B,6,3,256,704)` | ResNet-50 | FPN | BEV grid 3D bounding boxes `(B,N,cls+reg)` | |
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| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | NDS | 0.3603 | 0.3546 | 0.3564 | 0.3539 | |
| | | mAP | 0.2833 | 0.2799 | 0.2805 | 0.2797 | |
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|
| > Results measured with `march = March.NASH_M` (J6M) configuration. |
| > |
| > HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10. |
| |
| ### Performance Metrics |
| |
| > **Performance benchmark**: FPS is measured with single-core 8 threads; latency is single-core single-thread; memory is peak DDR usage. |
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| | March | latency (ms) | fps | Memory Usage | |
| |---|---|---|---| |
| | J6M | 16.45 | 62.04 | 121.20 | |
| | J6P | 11.42 | 347.23 | 123.10 | |
| | J6B | - | - | - | |
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| J6B performance is not available for this model. |
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| --- |
| |
| ## Model Overview |
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| ### Core Design |
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| This model follows the LSS (Lift-Splat-Shoot) view transformation approach: 6 camera images are processed by ResNet-50 + FPN for multi-scale features, projected to the BEV plane via LSSTransformer, encoded by BevEncoder, and finally predicted on the BEV grid by CenterPointHead for 3D bounding boxes. |
| |
| - **Task type**: BEV 3D object detection (BEV 3D Object Detection). |
| - **backbone**: ResNet-50 (`ResNet50`, `include_top=False`, pretrained `num_classes=1000`). |
| - **neck**: FPN (`FPN`, multi-scale feature pyramid, output strides 16/32, `fix_out_channel=256`). |
| - **Detection head**: `CenterPointHead` (BEV grid center-point detection head). |
| - **Loss function**: `CenterPointLoss` (GaussianFocalLoss + L1Loss). |
| - **Model input**: 6-camera multi-view images, `(B,6,3,256,704)` (original `orig_shape=(3,900,1600)` → resize `(3,396,704)` → crop `data_shape=(3,256,704)`, `num_views=6`). |
| - **Model output**: 3D bounding boxes on BEV grid (class + center + size + orientation), `CenterPointHead` outputs heatmap + reg/height/dim/rot/vel per task group, `num_classes=10`, decoded via `CenterPointPostProcess` + NMS. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/HuangJunJie2017/BEVDet |
| Paper: https://arxiv.org/pdf/2112.11790 |
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| Note: Backbone is ResNet-50; detection head is CenterPoint. |
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14085 |
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