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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+  - bev
+---
+
+# BEV (ResNet-50)
+
+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.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| 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)` |
+
+### Accuracy Metrics
+
+| 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 |
+
+> 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.
+
+| March | latency (ms) | fps | Memory Usage |
+|---|---|---|---|
+| J6M | 16.45 | 62.04 | 121.20 |
+| J6P | 11.42 | 347.23 | 123.10 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+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.
+
+### Official Repo and Paper
+
+Official repo: https://github.com/HuangJunJie2017/BEVDet
+Paper: https://arxiv.org/pdf/2112.11790
+
+Note: Backbone is ResNet-50; detection head is CenterPoint.