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


Metric Value
GFlops 260.1
MParams 31.03
Source framework PyTorch*


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


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


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

Legal Information

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