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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+  - bev
+---
+
+# BevFormer+HENet Multitask
+
+The BevFormer+HENet multitask model uses HENet as the camera backbone to extract multi-view features, then BevFormer ViewTransformer and temporal BEV Encoder to transform them into bird's-eye-view features. BEV features are fed to both a detection head (3D object detection) and an occupancy head (FlashOcc-style semantic occupancy prediction), enabling joint det+occ training.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| BevFusion | 6-camera multi-view image sequence `(B,6,3,512,960)` | HENet | FPN | det bounding boxes `(B,N,cls+reg)`; occ occupancy grid `(B,C,H,W)` |
+
+### Accuracy Metrics
+
+| March | Metric | float |
+| --- | --- | --- |
+| J6M | NDS | 0.3573 |
+|  | mAP | 0.3017 |
+|  | Occ mIoU | 0.3146 |
+
+> 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.
+
+---
+
+## Model Overview
+
+### Core Design
+
+The BevFormer+HENet multitask model uses HENet as the camera backbone to extract multi-view features, then BevFormer ViewTransformer and temporal BEV Encoder to transform them into bird's-eye-view features. BEV features are fed to both a detection head (3D object detection) and an occupancy head (FlashOcc-style semantic occupancy prediction), enabling joint det+occ training.
+
+- **Task type**: Multitask fusion (3D object detection + occupancy grid prediction).
+- **backbone**: HENet (camera feature extraction), extracting multi-view features.
+- **neck**: FPN.
+- **Detection head**: BevFormer detection head, outputting 10-class 3D bounding boxes + velocity (`num_classes=10`).
+- **Occupancy head**: Semantic occupancy prediction, 18 classes (`num_classes_occ=18`).
+- **BEV range**: `bev_size=(51.2, 51.2, 0.8)`, `bev_size_occ=(40, 40)`.
+- **Model input**: 6-camera image sequence `(B,6,3,512,960)` (`queue_length=1`).
+- **Model output**: det 3D bounding boxes + occ occupancy grid semantics `(B,C,H,W)`.
+
+### Official Repo and Paper
+
+Official repo: https://github.com/fundamentalvision/BevFormer
+Paper: https://arxiv.org/abs/2203.17270
+
+Note: The camera backbone HENet is a HEAL in-house implementation; the official repo uses a different backbone.