| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| --- |
| |
| # PETR (EfficientNet-b3) |
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| 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. |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | 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)` | |
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| ### Accuracy Metrics |
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| | 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 | |
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| > 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. |
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| | 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. |
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| --- |
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| ## Model Overview |
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| ### Core Design |
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| 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`. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/megvii-research/PETR |
| Paper: https://arxiv.org/abs/2203.05625 |
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| Note: The backbone is EfficientNet-b3; 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/14091 |
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