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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# PETR (EfficientNet-b3)
+
+PETR introduces 3D position into Transformer decoding: EfficientNet-b3 extracts multi-view image features, which are associated with 3D spatial positions via 3D positional encoding (SinePositionalEncoding3D); PETRTransformer queries interact directly with 3D position-encoded features to predict 3D detection boxes, without explicit BEV feature construction.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| PETR | 6-camera multi-view images `(B,6,3,512,1408)` | EfficientNet-b3 | — | Detection boxes `(B,900,21)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | NDS | 0.3881 | 0.3679 | 0.38 | 0.38 |
+|  | mAP | 0.3031 | 0.2807 | 0.2942 | 0.2942 |
+
+> Data 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 test methodology**: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage.
+
+| March | latency (ms) | fps | Memory Usage |
+|---|---|---|---|
+| J6M | 33.13 | 30.51 | 101.00 |
+| J6P | 21.56 | 186.12 | 105.30 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+PETR introduces 3D position into Transformer decoding: EfficientNet-b3 extracts multi-view image features, which are associated with 3D spatial positions via 3D positional encoding (SinePositionalEncoding3D); PETRTransformer queries interact directly with 3D position-encoded features to predict 3D detection boxes, without explicit BEV feature construction.
+
+- **Task type**: BEV 3D object detection (BEV 3D Object Detection).
+- **backbone**: EfficientNet-b3 (`efficientnet`, `model_type=b3`, `include_top=False` removes the classification head).
+- **neck**: — (no standalone neck; backbone features feed directly into `PETRHead`).
+- **Detection head**: `PETRHead` + `PETRTransformer` + `PETRDecoder` (3D position-aware Transformer decoder, `num_query=900`, `num_views=6`, `embed_dims=256`).
+- **Loss function**: FocalLoss (cls) + L1Loss (reg), with Detr3dTarget Hungarian matching.
+- **Model input**: 6-camera multi-view images, `data_shape=(3,512,1408)` (original `(3,900,1600)` resized to `(3,792,1408)` then cropped to `data_shape`), 6 views stacked as `(B,6,3,512,1408)`.
+- **Model output**: Detection boxes `(B,900,21)` — 900 queries × (10 class scores + 11 regression: center xyz + size wlh + orientation sin/cos + velocity vxvy), `num_classes=10`, `reg_out_channels=10`.
+
+### Official Repo and Paper
+
+Official repo: https://github.com/megvii-research/PETR
+Paper: https://arxiv.org/abs/2203.05625
+
+Note: The backbone is EfficientNet-b3; the official repo uses a different backbone.
+
+### Reference
+
+For more J6 chip deployment details, see https://developer.horizon.auto/blog/10373