train
folder and rename the gt
folder to ann
binary_masks
import optionimages
and ann
dirs to the upload windowINRIA
"action": "data"
) takes all data from project INRIA
and keeps classes as they are. Then there are five indentical branches for each town (Austin, Chicago, Kitsap County, Vienna and West Tyrol), so we will describe only one of them for Austin."action": "if"
) selects the data related to austin
, other data is sent to $null1
."action": "tag"
) adds tag austin
to each input image."action": "if"
) randomly splits the data into two branches: first branch - 14% (will be tagged as trainval
) and second branch - 86% (will be tagged as test
). We decreased the number of training images to demonstrate that entire pipline can be passed with a small number of annotated images."action": "tag"
) adds tag train
to all input images."action": "tag"
) adds tag val
to all input images ."action": "supervisely"
) saves results to the new project INRIA_TAGGED
."action": "data"
) takes all data from project INRIA_TAGGED
and keeps classes as they are."action": "if"
) splits the data into two branches based on tags. Further we will work only with the images which have tag trainval
, other images will be sent to null
."action": "sliding_window"
) creates crops of size 512x512 from the images using sliding window approach."action": "flip"
) flips data horisontally."action": "flip"
) flips data vertically."action": "if"
) randomly splits the data into two branches: first branch - 95% (will be tagged as train
) and second branch - 5% (will be tagged as val
)."action": "tag"
) adds the tag train
to all input images ."action": "tag"
) adds the tag val
to all input images."action": "if"
) filters the data (images without objects will be skipped)."action": "supervisely"
) saves results to the new project INRIA_train
. As a result we got around 7000 images for training.