icnet-camvid-ava-sparse-60-0001

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 60% 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, 3, 720, 960 in the BGR order. The pixel values are integers in the [0, 255] range.

The model output for icnet-camvid-ava-sparse-60-0001 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

69.91%

  • 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, 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.

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 60% 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, 3, 720, 960 in the BGR order. The pixel values are integers in the [0, 255] range.

The model output for icnet-camvid-ava-sparse-60-0001 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

69.91%

  • 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, 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.

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