Train/validation split for the baseline model

Hi,

The dataset that the mmseg baseline model is trained on is split into train/validation according to this code:

+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='inputs/train',
+        ann_dir='semantic_annotations/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='inputs/val',
+        ann_dir='semantic_annotations/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='inputs/val',
+        ann_dir='semantic_annotations/val',
+        pipeline=test_pipeline))

However, as when using the dataset download link it is not split into two folders “train” and “val” for both the inputs and masks. I have manually put them into a 84/16 split. Midway through my model training, I noticed that class “ANIMAL” and “SNOW” does not show up in the validation set.

image

Therefore, my questions are: how are they split in % and is it correct that the classes “ANIMAL” and “SNOW” are not present?