README.md

metadata

license: other
tags:
  - heal
  - horizon
  - bev

BEVFormer (ResNet-50)

BEVFormer extracts BEV features from multi-camera sequences via learnable spatiotemporal attention (Temporal Self-Attention + Spatial Cross-Attention): ResNet-50 + FPN extract multi-scale image features, BevFormerViewTransformer projects to BEV, BEVFormerEncoder fuses temporal and spatial information, and BEVFormerDetDecoder decodes 3D bounding boxes.

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Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
BevFormer	6-camera multi-view image sequence (B,6,3,480,800)	ResNet-50	FPN	3D bounding boxes (B,N,cls+reg)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	NDS	0.3739	0.3607	0.3734	0.3669

Results 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 benchmark: FPS is measured with single-core 8 threads; latency is single-core single-thread; memory is peak DDR usage.

March	latency (ms)	fps	Memory Usage
J6M	21.80	46.62	108.20
J6P	14.07	277.01	108.60
J6B	-	-	-

J6B performance is not available for this model.

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Model Overview

Core Design

BEVFormer extracts BEV features from multi-camera sequences via learnable spatiotemporal attention (Temporal Self-Attention + Spatial Cross-Attention): ResNet-50 + FPN extract multi-scale image features, BevFormerViewTransformer projects to BEV, BEVFormerEncoder fuses temporal and spatial information, and BEVFormerDetDecoder decodes 3D bounding boxes.

• Task type: BEV 3D object detection (BEV 3D Object Detection).
• backbone: ResNet-50 (ResNet50, include_top=False, pretrained num_classes=1000).
• neck: FPN (FPN, multi-scale feature pyramid, out_strides=[32], out_channels=[256]).
• Detection head: BEVFormerDetDecoder (DETR-style decoder + Hungarian matching).
• Loss function: BevFormerCriterion (FocalLoss + L1Loss, matched via BevFormerHungarianAssigner3D).
• Model input: 6-camera multi-view image sequence, (B,6,3,480,800) (original orig_shape=(3,900,1600) → resize (3,450,800) → pad to (3,480,800), num_views=6, training queue_length=3, evaluation queue_length=1).
• Model output: 3D bounding boxes on BEV features (class + center + size + orientation), num_query=900, num_classes=10, decoded via BevFormerProcess (max_num=300, score_threshold=0.3).

Official Repo and Paper

Official repo: https://github.com/fundamentalvision/BevFormer Paper: https://arxiv.org/abs/2203.17270

Reference

For more J6 chip deployment details, see https://developer.horizon.auto/blog/14101