OpenExploer/detr3d_efficientnetb3

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.

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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.

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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.