Downloads · 30 days
0
flashocc_henet_lss_occ3d
OpenExplorer/flashocc_henet_lss_occ3d
flashocc_henet_lss_occ3d is a machine learning model from OpenExplorer. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
FlashOcc uses LSS (Lift-Splat-Shoot) view transformation: HENet extracts multi-view camera features; LSSTransformer predicts depth distribution and lifts 2D features to 3D voxel space (depth=45, numpoints=10, bevsize=…
Access
Public
Updated Sep 1, 2026
Repo size
503 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bc280 MB · 45%
At a glance
- License
- other
- Access
- Public
- Created
- Aug 27, 2026
- Updated
- Sep 1, 2026
- SHA
- 79d62b50
Model card
From the Hugging Face model README
FlashOcc + HENet + LSS
FlashOcc uses LSS (Lift-Splat-Shoot) view transformation: HENet extracts multi-view camera features; LSSTransformer predicts depth distribution and lifts 2D features to 3D voxel space (depth=45, num_points=10, bev_size=(40,40,0.625)); fused via BevEncoder (BiFPN); FlashOccDetDecoder/BEVOCCHead2D outputs 18-class 3D occupancy predictions.
Deployment Metrics
Model Parameters
| Model | Model Input | Backbone | Neck | Model Output |
|---|---|---|---|---|
| FlashOcc | 6-camera multi-view images (B,6,3,512,960) | HENet | FPN + LSSTransformer + BevEncoder | Occupancy grid (B,C,H,W) |
Accuracy Metrics
| March | Metric | float | calibration | qat | hbm |
|---|---|---|---|---|---|
| J6M | Occ mIoU (MeanIOU) | 0.3664 | 0.3688 | — | 0.369 |
Results are based on
march = March.NASH_M(J6M) configuration; this task has no QAT stage (qat column is—).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 (MB) |
|---|---|---|---|
| J6M | 7.65 | 136.07 | 73.80 |
| J6P | 5.62 | 731.18 | 78.10 |
| J6B | 30.51 | 33.66 | 82.00 |
Model Overview
Core Design
FlashOcc uses LSS (Lift-Splat-Shoot) view transformation: HENet extracts multi-view camera features; LSSTransformer predicts depth distribution and lifts 2D features to 3D voxel space (depth=45, num_points=10, bev_size=(40,40,0.625)); fused via BevEncoder (BiFPN); FlashOccDetDecoder/BEVOCCHead2D outputs 18-class 3D occupancy predictions.
- Task type: BEV 3D occupancy prediction (BEV Occupancy Prediction).
- backbone: HENet (
type=HENet,depth=45,num_points=10), extracts multi-view camera features. - neck:
FPN+LSSTransformer(Lift-Splat-Shoot view transformation,bev_size=(40,40,0.625),grid_size=(128,128)) +BevEncoder(BiFPN). - Occupancy head:
FlashOccDetDecoder(BEVOCCHead2D,num_classes=18,ignore_index=17). - Loss:
CrossEntropyLoss(occ seg). - Model input: 6-view camera images
(B,6,3,512,960)(data_shape=(3,512,960)). - Model output: 18-class 3D occupancy grid
(B,C,H,W)(occ3d_seg_class18 classes, includesothers/ignore_index=17).
Official Repo and Paper
Official repo: https://github.com/Yzichen/FlashOCC Paper: https://arxiv.org/abs/2311.12058
Note: camera backbone HENet is HEAL-developed; official repo uses a different backbone.
Reference
For more J6 chip deployment details, see https://developer.horizon.auto/blog/14099
- License
- other
- Created
- Aug 27, 2026
- Updated
- Sep 1, 2026