Creating a new split

Before running experiments in Model Playground, you need to create a split.

To do so, please access Model Playground in the burger menu on the left and click the Create split button.

Parameters used for creating a new split

• Split name;
• Type of model you would like to experiment with - the list includes:
  • Attribute prediction;
  • Label class prediction;
  • Object detection;
  • Image tag prediction;
  • Instance segmentation;
  • Semantic segmentation.

• Split strategy - a strategy used to divide your dataset. The list includes:
  • Random sampling - the data is randomly allocated to the Test, Train, and Validation sets according to the specified proportions. One data point is assigned only to one set without repetition;
  • Stratification - this strategy preserves the proportion of each label in each set. It may be helpful if the dataset has a class imbalance.
  • Dataset partitioning - allows to assign each dataset to a particular partition. When selecting less than 100%, the algorithm will randomly sample the data from the selected datasets. Dataset partitioning is appropriate when you have already performed the data split and uploaded the partitions in separate datasets.

• Semi-Supervised Learning - this feature is available for Object Detection and Instance Segmentation tasks. Toggle it on to enable the feature.

• Part of a project to be used for split creation:
  • Image Statuses - select the files based on their status;
  • Datasets - select the datasets required for your experiment;
  • Classes - include particular classes in the split (optional);
  • Tags - include particular tags in the split (optional).

• Split percentage ratio - tweak the percentage of images to be divided into the Train, Test, and Validation datasets. The default percentages for the Train, Test, and Validation datasets are 70%, 20%, and 10%, respectively.