OpenExplorer/fcos_efficientnetb3

FCOS (EfficientNet-b3)

FCOS eliminates anchors and predicts classification scores, centerness, and distances to box edges per pixel; BiFPN performs bidirectional feature fusion across 5 scales to improve small-object detection; training jointly supervises classification, centerness, and regression losses.

---

Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
FCOS	Single image 1x3x896x896	EfficientNet-b3	BiFPN	Detection boxes (B,N,cls+reg)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	mAP	0.4802	0.4634	0.4773	0.4772

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	3.90	276.48	30.90
J6P	2.98	1439.97	35.10
J6B	-	-	-

J6B performance is not available for this model.

---

Model Overview

Core Design

FCOS eliminates anchors and predicts classification scores, centerness, and distances to box edges per pixel; BiFPN performs bidirectional feature fusion across 5 scales to improve small-object detection; training jointly supervises classification, centerness, and regression losses.

• Task type: 2D object detection (2D Object Detection).
• backbone: EfficientNet-b3 (efficientnet, model_type=b3, include_top=False removes classification head, use_se_block=False, ReLU activation).
• neck: BiFPN (out_channels=120, stack=6, input stride [2,4,8,16,32] → output stride [8,16,32,64,128], bifpn_sum fusion).
• Detection head: FCOSHead (feat_channels=120, stacked_convs=4, 5 feature layers stride [8,16,32,64,128], dequant_output=True).
• Target assignment: DynamicFcosTarget (topK=10, classification FocalLoss, regression GIoULoss).
• Post-processing: FCOSDecoder (score_thr=0.05, nms_pre=1000, NMS iou_threshold=0.6, max_per_img=100, uses centerness).
• Loss: Classification FocalLoss (alpha=0.25, gamma=2.0) + centerness CrossEntropyLoss (sigmoid) + regression GIoULoss.
• Model input: Single image, size 896 × 896.
• Model output: 80-class detection boxes + confidence scores.

Official Repo and Paper

Official repo: https://github.com/tianzhi0549/FCOS Paper: https://arxiv.org/abs/1904.01355

Note: backbone is EfficientNet-b3; official repo uses a different backbone.