Add README.md

README.md

@@ -0,0 +1,66 @@
+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# Detr3D (EfficientNet-b3)
+
+Detr3D brings the DETR paradigm to 3D detection: EfficientNet-b3 + BiFPN extract multi-scale image features; view transformation projects 2D features to 3D space; Detr3dTransformer iteratively samples multi-view features with learnable queries and predicts 3D detection boxes; Detr3dTarget performs Hungarian matching during training.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| Detr3D | 6-camera multi-view images `(B,6,3,512,1408)` | EfficientNet-b3 | BiFPN | 3D detection boxes `(B,N,cls+reg)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | NDS | 0.3357 | 0.3299 | 0.338 | 0.337 |
+|  | mAP | 0.2694 | 0.2618 | 0.2688 | 0.2683 |
+
+> Results are based on `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 measurement**: 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 | 21.88 | 46.43 | 97.60 |
+| J6P | 15.22 | 253.24 | 94.60 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+Detr3D brings the DETR paradigm to 3D detection: EfficientNet-b3 + BiFPN extract multi-scale image features; view transformation projects 2D features to 3D space; Detr3dTransformer iteratively samples multi-view features with learnable queries and predicts 3D detection boxes; Detr3dTarget performs Hungarian matching during training.
+
+- **Task type**: BEV 3D object detection (BEV 3D Object Detection).
+- **backbone**: EfficientNet-b3 (`efficientnet`, `model_type=b3`, `include_top=False`, `activation=relu`, `use_se_block=False`).
+- **neck**: BiFPN (`BiFPN`, bidirectional feature pyramid, `stack=3`, `out_channels=256`, `num_outs=5`).
+- **Detection head**: `Detr3dHead` + `Detr3dTransformer` + `Detr3dDecoder` (DETR-style 3D decoder).
+- **Loss**: FocalLoss (cls) + L1Loss (bbox), via Detr3dTarget Hungarian matching.
+- **Model input**: 6-camera multi-view images, `(B,6,3,512,1408)` (original `orig_shape=(3,900,1600)` → resize `(3,792,1408)` → crop `data_shape=(3,512,1408)`, `num_views=6`).
+- **Model output**: 3D detection boxes (class + center + size + orientation), `num_query=900`, `num_classes=10`, decoded via `Detr3dPostProcess` (`max_num=300`).
+
+### Official Repo and Paper
+
+Official repo: https://github.com/WangYueFt/detr3d
+Paper: https://arxiv.org/abs/2110.06922
+
+Note: backbone is EfficientNet-b3; official repo uses a different backbone.