EfficientNet-B0

EfficientNet-B0 consists of MBConv (mobile inverted bottleneck) convolution blocks, scaled jointly in depth/width/resolution via compound scaling; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).

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Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
EfficientNet-B0	1x3x224x224	EfficientNet-B0	—	Classification logits (B,1000)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	Accuracy	0.7491	0.7433	—	0.7436
	TopKAccuracy(5)	—	—	—	—

Results are based on march = March.NASH_M (J6M) configuration; this task has no QAT stage (qat column is —).

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	0.40	4938.45	8.90
J6P	0.35	10480.93	9.00
J6B	-	-	-

J6B performance is not available for this model.

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Model Overview

Core Design

EfficientNet-B0 consists of MBConv (mobile inverted bottleneck) convolution blocks, scaled jointly in depth/width/resolution via compound scaling; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).

• Task type: Image classification (Image Classification).
• backbone: EfficientNet-B0 (efficientnet, model_type="b0", activation="relu", use_se_block=False, num_classes=1000), composed of MBConv blocks scaled via compound scaling in depth/width/resolution; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).
• neck: — (EfficientNet-B0 has built-in fully-connected classification head; no separate neck).
• Classification head: EfficientNet-B0 built-in fully-connected classification head, directly outputs 1000-class logits.
• Loss: CEWithLabelSmooth (cross-entropy with label smoothing).
• Model input: Single RGB image, resolution 224 × 224 (1x3x224x224).
• Model output: 1000-class prediction logits; argmax gives predicted class.

Official Repo and Paper

Official repo: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet Paper: https://arxiv.org/abs/1905.11946