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license: other
tags:
- heal
- horizon
- occupancy
- lane-detection
---
# SparseMultitaskOE+MapTR+FlashOcc+HENet Multitask
SparseMultitaskOE uses HENet as backbone to extract multi-view features, converts to BEV features, then SparseBEVOEHead drives three task heads: detection head (SparseBEVOE detection, 10-class 3D boxes), map head (MapTR-style vectorized map, 3 element classes), occupancy head (FlashOcc-style semantic occupancy, 18 classes). This task uses three-stage Float training: stage1 jointly trains det+map+occ (backbone), stage2 freezes backbone and trains map+occ heads only (`lr=1e-5`), merge stage combines stage1 (det) and stage2 (map/occ) weights into a complete model. `use_lidar_gt=True`, `defer_vectormap=True` (map generated online).
---
## Deployment Metrics
### Model Parameters
| Model | Model Input | Backbone | Neck | Model Output |
|---|---|---|---|---|
| SparseMultitaskOE | 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` | HENet-tiny | MMFPN | det detection boxes `(B,N,cls+reg)`; map vectorized map `(B,L,P,2)`; occ occupancy grid `(B,C,H,W)` |
### Accuracy Metrics
| March | Metric | float | calibration | qat | hbm |
| --- | --- | --- | --- | --- | --- |
| J6M | NDS | 0.5434 | 0.5325 | — | 0.5281 |
| | mAP | — | — | — | — |
| | chamfer mAP (MAP) | 0.592 | 0.5838 | — | 0.5833 |
| | Occ mIoU | 0.3197 | 0.3277 | — | 0.3273 |
> Data 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 test methodology**: FPS for J6M/J6P is single-core eight-thread; J6B is single-core dual-thread; Latency is single-core single-thread; Memory is peak DDR usage.
| March | latency (ms) | fps | Memory Usage |
|---|---|---|---|
| J6M | 27.65 | 36.66 | 183.60 |
| J6P | 17.75 | 188.57 | 213.50 |
| J6B | 201.07 | 7.88 | 122.00 |
---
## Model Overview
### Core Design
SparseMultitaskOE uses HENet as backbone to extract multi-view features, converts to BEV features, then SparseBEVOEHead drives three task heads: detection head (SparseBEVOE detection, 10-class 3D boxes), map head (MapTR-style vectorized map, 3 element classes), occupancy head (FlashOcc-style semantic occupancy, 18 classes). This task uses three-stage Float training: stage1 jointly trains det+map+occ (backbone), stage2 freezes backbone and trains map+occ heads only (`lr=1e-5`), merge stage combines stage1 (det) and stage2 (map/occ) weights into a complete model. `use_lidar_gt=True`, `defer_vectormap=True` (map generated online).
- **Task type**: Multitask fusion (3D object detection + vectorized map construction + occupancy grid prediction).
- **backbone**: HENet-tiny (pretrained).
- **neck**: MMFPN.
- **Detection head**: `SparseBEVOEHead` (`SparseBEVOEEncoder` + `SparseBEVOERefinementModule`, 10-class 3D detection boxes, `num_classes=10`).
- **Map head**: `SparseMapPerceptionDecoder` (`SparseMapHead`, 3-class vectorized map elements, `map_classes=[divider,ped_crossing,boundary]`).
- **Occupancy head**: Semantic occupancy prediction, 18 classes (`num_classes_occ=18`).
- **BEV range**: `point_cloud_range=[-51.2,-51.2,-5.0,51.2,51.2,3.0]` (det/occ), `map_point_cloud_range=[-15.0,-30.0,-10.0,15.0,30.0,10.0]` (map), `occ_bev_size=(40,40,0.625)`.
- **Model input**: 6-camera multi-view images (B,6,3,256,704) + lidar point cloud (B,N,D).
- **Model output**: det 3D detection boxes + map vectorized map elements + occ occupancy grid semantics.
**Deployment notes**: HBIR export enables `enable_vpu=True`; compilation uses `input_source=compile_cfg["input_source"]`.
### Official Repo and Paper
Official repo: det SparseBEV/map MapTR/occ FlashOcc
https://github.com/MCG-NJU/SparseBEV, https://github.com/hustvl/MapTR, https://github.com/Yzichen/FlashOCC
Paper: https://arxiv.org/abs/2308.09244, https://arxiv.org/abs/2208.14437, https://arxiv.org/abs/2311.12058
Note: Camera backbone HENet is HEAL in-house; upstream papers for det/map/occ are SparseBEV/MapTR/FlashOcc respectively.
### Reference
For more J6 chip deployment details, see https://developer.horizon.auto/blog/14095
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