| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - bev |
| --- |
| |
| # SparseBEV + HENet-tiny |
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| SparseBEV performs 3D detection in BEV space via sparse queries: HENet-tiny extracts multi-view camera features, `MMFPN` fuses multi-scale features, `DenseDepthNet` uses lidar point clouds for dense depth supervision during training; `SparseBEVHead` refines sparse queries layer by layer via `MemoryBank` + `SparseBEVEncoder` (with `AsymmetricFFN`, `DeformableFeatureAggregation`, `SparseBEVRefinementModule`) and outputs detection boxes. |
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| --- |
|
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| ## Deployment Metrics |
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|
| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | SparseBEV | 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` | HENet-tiny | MMFPN + DenseDepthNet | 3D bounding boxes `(B,N,cls+reg)` | |
|
|
| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | NDS | 0.5414 | 0.5319 | — | 0.5307 | |
| | | mAP | 0.4338 | 0.4265 | — | 0.4235 | |
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|
| > Results measured with `march = March.NASH_M` (J6M) configuration; this task has no QAT stage (qat column is `—`). |
| > |
| > 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. |
| |
| | March | latency (ms) | fps | Memory Usage (MB) | |
| |---|---|---|---| |
| | J6M | 10.44 | 98.74 | 74.70 | |
| | J6P | 7.39 | 524.54 | 87.80 | |
| | J6B | 28.57 | 36.16 | 72.00 | |
| |
| --- |
| |
| ## Model Overview |
| |
| ### Core Design |
| |
| SparseBEV performs 3D detection in BEV space via sparse queries: HENet-tiny extracts multi-view camera features, `MMFPN` fuses multi-scale features, `DenseDepthNet` uses lidar point clouds for dense depth supervision during training; `SparseBEVHead` refines sparse queries layer by layer via `MemoryBank` + `SparseBEVEncoder` (with `AsymmetricFFN`, `DeformableFeatureAggregation`, `SparseBEVRefinementModule`) and outputs detection boxes. |
| |
| - **Task type**: BEV 3D object detection (BEV 3D Object Detection). |
| - **backbone**: HENet-tiny (`type=HENet`, `in_channels=3`, 4 stages `[4,3,8,6]` blocks, `embed_dims=[64,128,192,384]`, `GroupDWCB`/`AltDWCB`/`DWCB` blocks, `S2DDown` downsampling; multi-view camera feature extraction). |
| - **neck**: `MMFPN` (`in_strides=[2,4,8,16,32]`→`out_strides=[4,8,16,32]`, multi-scale fusion) + `DenseDepthNet` (dense depth supervision from lidar point clouds during training, `num_depth_layers=3`). |
| - **Detection head**: `SparseBEVHead` (`MemoryBank` + `SparseBEVEncoder` + `SparseBEVEncoder`, with `AsymmetricFFN`, `DeformableFeatureAggregation`, `SparseBEVRefinementModule`, layer-wise sparse query refinement, `num_classes=10`, `num_decoder=6`, `num_anchors=384`). |
| - **Loss function**: `FocalLoss` (cls) + `L1Loss` (reg) + `CrossEntropyLoss` (cns) + `GaussianFocalLoss` (yns). |
| - **Model input**: 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` (`load_dim=5`, `use_dim=[0,1,2,4]`, `num_lidar_sweeps=0`, dense depth GT auxiliary). |
| - **Model output**: 10-class 3D bounding boxes (`num_classes=10`), `(B,N,cls+reg)`. |
|
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| ### Official Repo and Paper |
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| Official repo: https://github.com/MCG-NJU/SparseBEV |
| Paper: https://arxiv.org/abs/2308.09244 |
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| Note: The camera backbone HENet is a HEAL in-house implementation; the official repo uses a different backbone. |
|
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14074 |
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