In Supervisely all data and annotations are stored inside individual projects which themselves consist of datasets with files in them, and Project Meta - series of classes and tags.
When downloaded, each project is converted into a folder that stores
meta.json file containing Project Meta, dataset folders with the individual annotation files (and optionally the original data files) in them. This allows you to seamlessly cycle data between Supervisely and local storage with the use of
Supervisely Format import plugin, if you so require.
This structure remains the same for every type of project in Supervisely.
On the top level we have Project folders, these are the elements visible on the main Supervisely dashboard. Inside them they can contain only Datasets and Poject Meta information, all other data has to be stored a level below in a Dataset. All datasets within a project have to contain content of the same cathegory.
Project Meta contains the essential information about the project - Classes and Tags. These are defined project-wide and can be used for labeling in every dataset inside the current roject.
Datasets are the second level folders inside the project, they host the individual data files and their annotations.
Every data file in the project has to be stored inside a dataset. Each file as it's own set of annotations.
All projects downloaded from Supervisely maintain the same basic structure, with the contents varying based on which download option you chose.
When you select one of the download option, the system automatically creates an archive with the following name structure:
All projects downloaded from Supervisely have the following structure:
Root folder for the project named
obj_class_to_machine_color.json file (optional, for image annotation projects)
key_id_map.json file (optional)
Dataset folders, each named
dataset_name, which contains:
ann folder, contains annotation files, each named
source_media_file_name.json for the corresponding file
pointcloud) optional folder, contains source media
masks_human optional folder for image annotation projects, contains .png files with annotations marked on them
masks_machine optional folder for image annotation projects, contains .png files with machine annotations