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icnet-camvid-ava-sparse-30-0001

Use Case and High-Level Description

A trained model of ICNet for fast semantic segmentation, trained on the CamVid dataset from scratch using the TensorFlow* framework. The trained model has 30% sparsity (ratio of zeros within all the convolution kernel weights). For details about the original floating-point model, check out the ICNet for Real-Time Semantic Segmentation on High-Resolution Images.

The model input is a blob that consists of a single image of 1, 720, 960, 3 in the BGR order. The pixel values are integers in the [0, 255] range.

The model output for icnet-camvid-ava-sparse-30-0001 is the predicted class index 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

75.8180

MParams

26.7043

Source framework

TensorFlow*

Accuracy

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

Metric

Value

mIoU

75.87%

  • 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, name: data, shape - 1, 720, 960, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is BGR.

Output

Semantic segmentation class prediction map, shape - 1, 720, 960, output data format is B, H, W, where:

  • B - batch size

  • H - horizontal coordinate of the input pixel

  • W - vertical coordinate of the input pixel

Output contains the class prediction result of each 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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