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 1x3x368x480 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.


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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