unet_mobilenetv1

UNet (MobileNetV1)

UNet encoder-decoder structure: MobileNetV1 extracts multi-scale features (stride 4/8/16/32/64); DwUnet upsamples level-by-level with depthwise separable convolutions and fuses feature maps; SegHead outputs segmentation logits at 5 scales; SoftmaxFocalLoss provides joint supervision; at deployment, SegHead exports only the highest-resolution (stride 4) scale argmax result.

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Deployment Metrics

Model Parameters

Accuracy Metrics

Data measured with 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 test methodology: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage.

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Model Overview

Core Design

UNet encoder-decoder structure: MobileNetV1 extracts multi-scale features (stride 4/8/16/32/64); DwUnet upsamples level-by-level with depthwise separable convolutions and fuses feature maps; SegHead outputs segmentation logits at 5 scales; SoftmaxFocalLoss provides joint supervision; at deployment, SegHead exports only the highest-resolution (stride 4) scale argmax result.

• Task type: Semantic segmentation (Semantic Segmentation).
• backbone: MobileNetV1 (alpha=0.25, include_top=False removes classification head, lightweight depthwise separable convolution backbone).
• neck: DwUnet (base_channels=8, output_scales=(4,8,16,32,64), depthwise separable convolution U-Net decoder).
• Segmentation head: SegHead (num_classes=19, outputs at stride 4/8/16/32/64 (5 scales); training outputs logits, deployment outputs argmax).
• Loss function: SoftmaxFocalLoss (scale-weighted, mitigates class imbalance).
• Normalization: BGR→YUV444 then Normalize(mean=128, std=128), then split into multi-scale pyramid input via scale_factors.
• Model input: 1×3×1024×2048 (RGB, native resolution).
• Model output: Per-pixel 19-class semantic segmentation map (B,19,H,W).

Official Repo and Paper

Official repo: https://github.com/pytorch/vision (MobileNetV1 PyTorch implementation) Paper: https://arxiv.org/abs/1704.04861

Note: UNet paper at https://arxiv.org/abs/1505.04597.