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unet-camvid-onnx-0001

Use Case and High-Level Description

This is a U-Net model that is designed to perform semantic segmentation. The model has been trained on the CamVid dataset from scratch using PyTorch* framework. Training used median frequency balancing for class weighing. For details about the original floating-point model, check out U-Net: Convolutional Networks for Biomedical Image Segmentation.

The model input is a blob that consists of a single image of 1, 3, 368, 480 in the BGR order. The pixel values are integers in the [0, 255] range.

The model output for unet-camvid-onnx-0001 is the per-pixel probabilities of each input pixel belonging to one of the 12 classes of the CamVid dataset:

  • Sky

  • Building

  • Pole

  • Road

  • Pavement

  • Tree

  • SignSymbol

  • Fence

  • Vehicle

  • Pedestrian

  • Bike

  • Unlabeled

Specification

Metric

Value

GFlops

260.1

MParams

31.03

Source framework

PyTorch*

Accuracy

The quality metrics were calculated on the CamVid validation dataset. The unlabeled class had been ignored during metrics calculation.

Metric

Value

mIoU

71.95%

  • IOU=TP/(TP+FN+FP), where:

    • TP - number of true positive pixels for given class

    • FN - number of false negative pixels for given class

    • FP - number of false positive pixels for given class

Input

Image, shape - 1, 3, 368, 480, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR

Output

Semantic segmentation class probabilities map, shape -1, 12, 368, 480, output data format is B, C, H, W, where:

  • B - batch size

  • C - predicted probabilities of input pixel belonging to class C in the [0, 1] range

  • H - horizontal coordinate of the input pixel

  • W - vertical coordinate of the input pixel

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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