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:
The quality metrics were calculated on the CamVid validation dataset. The
unlabeled class had been ignored during metrics calculation.
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
data, shape -
1, 720, 960, 3, format is
B, H, W, C, where:
B- batch size
Channel order is
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.
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.