|Description (include details on usage, files and paper references)||The MSRC v2 dataset is an extension of the MSRC v1 dataset from Microsoft Research in Cambridge. It contains 591 images and 23 object classes with accurate pixel-wise labeled images. Though it contains 23 object classes, only 21 classes are commonly used. The unused labels are (void==0, horse==5, mountain==8) due to background or too few training samples.
The dataset is commonly used for full scene segmentation, and may also be used for object instance segmentation, as the current annotation also contains individual object instances next to pure class annotation.
The training is done on 276X images, validation on 59 and testing on 275 images. The classes should be equally spread among the 45%, 10% and 45% splits. There exists an example split from Jamie Shotton, and the code for TextonBoost has routines for generating new splits.
Evaluation is done using an average pixel-wise, class-average and PASCAL class-wise accuracy, ignoring the three classes as mentioned before to only evaluate 21 classes.