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 1x3x720x960 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.

Specification

Accuracy

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

• 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

Performance

Input

Image, shape - 1,3,720,960, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

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.

Legal Information

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