docs: sync model card to heal 0.14.0 with J6M/J6P perf

README.md

@@ -8,7 +8,9 @@ tags:
 
 # BEV Multitask + HENet-tiny
 
-`bev_multitask_henet_tinym` is a camera-only BEV multitask perception model: HENet-tiny backbone + single-frame BevFormer view transformer, jointly performing five tasks — dynamic object 3D detection (with tracking), static object 3D detection, occupancy prediction, discrete map (stop line) detection, and online vectorized map (MapTR). It covers the full pipeline from 3-stage float training → quantization calibration → HBIR export → HBM compilation → inference.
+A camera-only BEV multitask model for 3D detection, occupancy, stop-line detection, and online lane-level mapping.
+
+BEV Multitask + HENet-tiny lifts six surround cameras into a shared bird’s-eye feature and runs five heads together—dynamic/static objects, occupancy, stop lines, and vectorized map—so scene cues stay consistent. This release is calibrated for Horizon J6M / J6P deployment without lidar at inference.
 
 ---
 
@@ -28,7 +30,7 @@ tags:
 
 > Accuracy numbers not yet measured; fill in after evaluation. Results use `march = March.NASH_M` (J6M) configuration; this task has no QAT stage (qat column is `—`).
 >
-> HEAL version: heal 0.6.0 / hbdk4-compiler 4.13.1 / horizon_plugin_pytorch 3.4.5 (board UCP 3.16.2).
+> HEAL version: heal 0.14.0 / hbdk4-compiler 4.13.1 / horizon_plugin_pytorch 3.4.5 (board UCP 3.16.2).
 
 ### Performance Metrics
 
@@ -37,7 +39,8 @@ tags:
 
 | March | latency (ms) | fps | Memory Usage (MB) |
 |---|---|---|---|
-| J6M | — | — | — |
+| J6M | 77.82 | 13.21 | 386.90 |
+| J6P | 47.65 | 76.17 | 373.50 |
 
 ---
 
@@ -45,7 +48,7 @@ tags:
 
 ### Core Design
 
-HENet-tiny extracts multi-view camera features; a single-frame `SingleBevFormerViewTransformer` (with `LearnedPositionalEncoding`, `HorizonSpatialCrossAttention` and `HorizonMultiScaleDeformableAttention`) lifts them to a `128×128` BEV grid covering `[-51.2m, 51.2m]²`. The BEV feature drives five task heads: dynamic/static 3D detection (`DenseBEVSparseHead`, SparseBEV-style sparse queries with `MemoryBank` temporal tracking), occupancy (`BEVOCCHead2D`, 18 classes), discrete map (`BEVFormerDet2DDecoder`, stop line), and online vectorized map (`MaptrDetDecoder`, 3 classes).
+Six surround cameras are encoded by HENet-tiny, then lifted into a compact BEV canvas (about 100 m around the ego) with a single-frame BevFormer view transformer. That shared BEV feature fans out to five lightweight heads: SparseBEV-style queries for dynamic and static 3D boxes, a voxel occupancy head, a stop-line detector, and a MapTR-style polyline head for dividers, crossings, and boundaries — so detection, free space, and map cues stay consistent without running five separate networks.
 
 - **Task type**: BEV multitask perception (dynamic det + static det + occupancy + discrete map + online map).
 - **backbone**: HENet-tiny (`type=HENet`, `block_nums=[4,3,8,6]`, `embed_dims=[64,128,192,384]`, `GroupDWCB`/`AltDWCB`/`DWCB` blocks, `S2DDown` downsampling), initialized from the ImageNet-pretrained `henet_tinym` float checkpoint.