# 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

• Pavement

• Tree

• SignSymbol

• Fence

• Vehicle

• Pedestrian

• Bike

• Unlabeled

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