cityscapes_orig
.cityscapes_orig
contains 5000 images in total: "action": "data"
) merges two classes (rider
and person
) to the single one (person_poly
) and all other classes to the single class unnec
. We do not drop unnecessary classes because they are needed to make the correct rasterization."action": "rasterize"
) performs the correct rasterization and converts the class types to bitmap. Read more here."action": "supervisely"
) saves results to the new project with the name cityscapes_rasterized_person
."action": "data"
) merges the entire cityscapes_rasterized_person
project and map person
into person_b
."action": "if"
) drops all images that have no objects."action": "bbox"
) converts objects of the class person_b
from bitmaps to bounding boxes and renames this class as person
."action": "resize"
) resizes image to the "height": 600
."action": "objects_filter"
) drops objects of the class person
that are less then 10px * 10px."action": "if"
) drops all images without any objects again."action": "supervisely"
) saves the results to the new project with the name city_person_filtered_boxes
.mapillary
."action": "data"
) merges classes (Person
, Bicyclist
, Other Rider
and Motorcyclist
) into a single class (person_b
) and drops all other classes."action": "if"
) drops all images that have no objects."action": "bbox"
) convert objects of class person_b
from bitmaps to bounding boxes and renames this class as person
."action": "resize"
) resizes image to the "height": 600
."action": "objects_filter"
) drops objects of class person
that are less then 10px * 10px."action": "if"
) drops all images without any objects again."action": "supervisely"
) saves the results to the new project with name mapillary_filtered_boxes
.city_person_filtered_boxes
) and transformed Mapillary project (mapillary_person_filtered_boxes
),"action": "data"
) gets everything from the project mapillary_person_filtered_boxes
and keeps the classes as they are."action": "data"
) gets everything from the project city_person_filtered_boxes
and keeps classes as they are."action": "dummy"
) combines data from Layer #1 and Layer #2 into a single variable."action": "flip"
) applies a vertical flip to the images and their annotations."action": "crop"
) performs relatively big random crops (from 60% to 90% in width and height with respect to the image size)"action": "if"
) drops all images without any objects of class person
."action": "if"
) randomly split the data into two branches: first branch - 98% (will be tagged as train
) and second branch - 2% (will be tagged as val
)"action": "tag"
) adds the tag train
to all input images."action": "tag"
) add the tag val
to all input images."action": "supervisely"
) saves results to the new project with name train_detector
.train_detector_final
that will be used for training (41615 images with 175088 objects)."action": "data"
) gets everything from the project train_detector
and keeps classes as they are."action": "objects_filter"
) drops objects of the class person
that are less then 1px * 1px (drops "dummy" objects)."action": "if"
) drops all images without any objects of class person
."action": "supervisely"
) saves results to the new project with the name train_detector_final
.