OpenExplorer/bevformer_tiny_resnet50_detection

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.

Deployment Metrics

Model Parameters

Accuracy Metrics

Results measured with march = March.NASH_M (J6M) configuration. HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizonpluginpytorch 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.

J6B performance is not available for this model.

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