OpenExploer/fcos3d_efficientnetb0

FCOS3D (EfficientNet-b0)

FCOS3D extends FCOS anchor-free fully-convolutional detection to 3D: EfficientNet-b0 + BiFPN extract multi-scale features; FCOS3DHead predicts 3D detection boxes (center offset, depth, size, orientation, class) at each feature point; FCOS3DTarget performs 3D target assignment; FCOS3DPostProcess decodes final 3D detection boxes.

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Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
FCOS3D	Single front-view image (B,3,512,896)	EfficientNet-b0	BiFPN	Front-view 3D detection boxes (B,N,cls+reg)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	NDS	0.312	0.3044	0.3099	0.3098
	mono mAP	0.2101	0.2042	0.2067	0.207

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	1.89	652.39	18.40
J6P	1.49	3310.89	19.10
J6B	6.59	183.42	14.00

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Model Overview

Core Design

FCOS3D extends FCOS anchor-free fully-convolutional detection to 3D: EfficientNet-b0 + BiFPN extract multi-scale features; FCOS3DHead predicts 3D detection boxes (center offset, depth, size, orientation, class) at each feature point; FCOS3DTarget performs 3D target assignment; FCOS3DPostProcess decodes final 3D detection boxes.

• Task type: Monocular 3D object detection (Monocular 3D Object Detection).
• backbone: EfficientNet-b0 (efficientnet, model_type=b0, include_top=False, activation=relu, use_se_block=False).
• neck: BiFPN (BiFPN, bidirectional feature pyramid, stack=3, out_channels=64, num_outs=5).
• Detection head: FCOS3DHead (fully-convolutional anchor-free 3D detection head).
• Loss: FCOS3DLoss (FocalLoss + SmoothL1Loss + CrossEntropyLoss).
• Model input: Single front-view image (only CAM_FRONT from 6 cameras), (B,3,512,896) (Resize3D img_scale=(896,512) + Pad (512,896)).
• Model output: Front-view 3D detection boxes (class + center + size + orientation), per-feature-point prediction group_reg_dims=(2,1,3,1,2), num_classes=10, decoded via FCOS3DPostProcess + NMS (max_per_img=100).

Official Repo and Paper

Official repo: https://github.com/open-mmlab/mmdetection3d Paper: https://arxiv.org/abs/2104.10956

Note: backbone is EfficientNet-b0; official implementation uses a different backbone.

Reference

For more J6 chip deployment details, see https://developer.horizon.auto/blog/10372