Transform#

Transform Dataset#

Often datasets need to be modified during preparation for model training and experimenting. In trivial cases it can be done manually - e.g. image renaming or label renaming. However, in more complex cases even simple modifications can require too much efforts, distracting the user from the real work. Datumaro provides the datum transform command to help in such cases.

This command allows to modify dataset images or annotations all at once.

This command is designed for batch dataset processing, so if you only need to modify few elements of a dataset, you might want to use other approaches for better performance. A possible solution can be a simple script, which uses Datumaro API.

The command can be applied to a dataset or a project build target, a stage or the combined project target, in which case all the project targets will be affected. A build tree stage will be recorded if --stage is enabled, and the resulting dataset(-s) will be saved if --apply is enabled.

By default, datasets are updated in-place. The -o/--output-dir option can be used to specify another output directory. When updating in-place, use the --overwrite parameter (in-place updates fail by default to prevent data loss), unless a project target is modified.

The current project (-p/--project) is also used as a context for plugins, so it can be useful for dataset paths having custom formats. When not specified, the current project’s working tree is used.

Usage:

datum transform [-h] -t TRANSFORM [-o DST_DIR] [--overwrite]
  [-p PROJECT_DIR] [--stage STAGE] [--apply APPLY] [target] [-- EXTRA_ARGS]

Parameters:

<target> (string) - Target dataset revpath. By default, transforms all targets of the current project.
-t, --transform (string) - Transform method name
--stage (bool) - Include this action as a project build step. If true, this operation will be saved in the project build tree, allowing to reproduce the resulting dataset later. Applicable only to main project targets (i.e. data sources and the project target, but not intermediate stages). Enabled by default.
--apply (bool) - Run this command immediately. If disabled, only the build tree stage will be written. Enabled by default.
-o, --output-dir (string) - Output directory. Can be omitted for main project targets (i.e. data sources and the project target, but not intermediate stages) and dataset targets. If not specified, the results will be saved inplace.
--overwrite - Allows to overwrite existing files in the output directory, when it is specified and is not empty.
-p, --project (string) - Directory of the project to operate on (default: current directory).
-h, --help - Print the help message and exit.
<extra args> - The list of extra transformation parameters. Should be passed after the -- separator after the main command arguments. See transform descriptions for info about extra parameters. Use the --help option to print parameter info.

Examples:

Split a VOC-like dataset randomly

datum transform -t random_split --overwrite path/to/dataset:voc

Rename images in a project data source by a regex from frame_XXX to XXX

NOTE: Please use double quotes (") for regex representation. Check Reason to use double quotes.
```
datum project create <...>
datum project import <...> -n source-1
datum transform -t rename source-1 -- -e "|^frame_||"
```

Built-in transforms#

Basic dataset item manipulations:

rename - Renames dataset items by regular expression
id_from_image_name - Renames dataset items to their image filenames
reindex - Renames dataset items with numbers
sort - Sort dataset items
ndr - Removes duplicated images from dataset
relevancy_sampler - Leaves only the most important images (requires model inference results)
random_sampler - Leaves no more than k items from the dataset randomly
label_random_sampler - Leaves at least k images with annotations per class
resize - Resizes images and annotations in the dataset
remove_images - Removes specific images
remove_annotations - Removes annotations
remove_attributes - Removes attributes
astype_annotations - Transforms annotation types
pseudo_labeling - Generates pseudo labels for unlabeled data
correct - Corrects annotation types
clean - Removes noisy data for tabular dataset

Subset manipulations:

random_split - Splits dataset into subsets randomly
split - Splits dataset into subsets for classification, detection, segmentation or re-identification
map_subsets - Renames and removes subsets

Annotation manipulations:

remap_labels - Renames, adds or removes labels in dataset
project_labels - Sets dataset labels to the requested sequence
shapes_to_boxes - Replaces spatial annotations with bounding boxes
boxes_to_masks - Converts bounding boxes to instance masks
polygons_to_masks - Converts polygons to instance masks
masks_to_polygons - Converts instance masks to polygons
anns_to_labels - Replaces annotations having labels with label annotations
merge_instance_segments - Merges grouped spatial annotations into a mask
crop_covered_segments - Removes occluded segments of covered masks
bbox_value_decrement - Subtracts 1 from bbox coordinates

`rename`#

Renames items in the dataset. Supports regular expressions. The first character in the expression is a delimiter for the pattern and replacement parts. Replacement part can also contain str.format replacement fields with the item (of type DatasetItem) object available.

Usage:

rename [-h] [-e REGEX]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-e, --regex (string) - Regex for renaming in the form <sep><search><sep><replacement><sep>

Examples:

Replace ‘pattern’ with ‘replacement’

datum transform -t rename -- -e "|pattern|replacement|"

Remove the frame_ prefix from item ids

datum transform -t rename -- -e "|^frame_|"

Collect images from subdirectories into the base image directory using regex
```
datum transform -t rename -- -e "|^((.+[/\\])*)?(.+)$|\2|"
```

Add subset prefix to images

datum transform -t rename -- -e "|(.*)|{item.subset}_\1|"

`id_from_image_name`#

Renames items in the dataset based on the image file name, excluding the extension. When ‘ensure_unique’ is enabled, a random suffix is appended to ensure each identifier is unique in cases where the image name is not distinct. By default, the random suffix is three characters long, but this can be adjusted with the ‘suffix_length’ parameter.

Usage:

id_from_image_name [-h] [-u] [-l SUFFIX_LENGTH]

Optional arguments:

-h, --help (flag) - show this help message and exit
-u, --ensure_unique (flag) - Appends a random suffix to ensure each identifier is unique if the image name is duplicated
-l, --suffix_length (int) - Alters the length of the random suffix if the ensure_unique is enabled(default: 3)

Examples:

Renames items without duplication check
```
datum transform -t id_from_image_name
```

Renames items with duplication check

datum transform -t id_from_image_name -- --ensure_unique

Renames items with duplication check and alters the suffix length(default: 3)
```
datum transform -t id_from_image_name -- --ensure_unique --suffix_length 2
```

`reindex`#

Replaces dataset item IDs with sequential indices.

Usage:

reindex [-h] [-s START]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-s, --start (int) - Start value for item ids (default: 1)

`sort`#

Sorts dataset items.

Usage:

reindex [-h] [-s START]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-k, --key (string/callable) - key function to sort (default: sorted by item.id)

Examples:

Sort by id converted into integer

datum transform -t ndr -- --key "lambda item: int(item.id)"

`ndr`#

Removes near-duplicated images in subset.

Remove duplicated images from a dataset. Keep at most -k/--num_cut resulting images.

Available oversampling policies (the -e parameter):

random - sample from removed data randomly
similarity - sample from removed data with ascending similarity score

Available undersampling policies (the -u parameter):

uniform - sample data with uniform distribution
inverse - sample data with reciprocal of the number of number of items with the same similarity

Usage:

ndr [-h] [-w WORKING_SUBSET] [-d DUPLICATED_SUBSET] [-a {gradient}]
    [-k NUM_CUT] [-e {random,similarity}] [-u {uniform,inverse}] [-s SEED]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-w, --working_subset (str) - Name of the subset to operate (default: None)
-d, --duplicated_subset (str) - Name of the subset for the removed data after NDR runs (default: duplicated)
-a, --algorithm (one of: gradient) - Name of the algorithm to use (default: gradient)
-k, --num_cut (int) - Maximum output dataset size
-e, --over_sample (one of: random, similarity) - The policy to use when num_cut is bigger than result length (default: random)
-u, --under_sample (one of: uniform, inverse) - The policy to use when num_cut is smaller than result length (default: uniform)
-s, --seed (int) - Random seed

Examples:

Apply NDR, return no more than 100 images

datum transform -t ndr -- \
  --working_subset train
  --algorithm gradient
  --num_cut 100
  --over_sample random
  --under_sample uniform

`relevancy_sampler`#

Sampler that analyzes model inference results on the dataset and picks the most relevant samples for training.

Creates a dataset from the -k/--count hardest items for a model. The whole dataset or a single subset will be split into the sampled and unsampled subsets based on the model confidence. The dataset must contain model confidence values in the scores attributes of annotations.

There are five methods of sampling (the -m/--method option):

topk - Return the k items with the highest uncertainty data
lowk - Return the k items with the lowest uncertainty data
randk - Return random k items
mixk - Return a half using topk, and the other half using lowk method
randtopk - Select 3*k items randomly, and return the topk among them

Notes:

Each image’s inference result must contain the probability for all classes (in the scores attribute).
Requesting a sample larger than the number of all images will return all images.

Usage:

relevancy_sampler [-h] -k COUNT [-a {entropy}] [-i INPUT_SUBSET]
                  [-o SAMPLED_SUBSET] [-u UNSAMPLED_SUBSET]
                  [-m {topk,lowk,randk,mixk,randtopk}] [-d OUTPUT_FILE]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-k, --count (int) - Number of items to sample
-a, --algorithm (one of: entropy) - Sampling algorithm (default: entropy)
-i, --input_subset (str) - Subset name to select sample from (default: None)
-o, --sampled_subset (str) - Subset name to put sampled data to (default: sample)
-u, --unsampled_subset (str) - Subset name to put the rest data to (default: unsampled)
-m, --sampling_method (one of: topk, lowk, randk, mixk, randtopk) - Sampling method (default: topk)
-d, --output_file (path) - A .csv file path to dump sampling results

Examples:

Select the most relevant data subset of 20 images based on model certainty, put the result into sample subset and put all the rest into unsampled subset, use train subset as input. The dataset must contain model confidence values in the scores attributes of annotations.
```
datum transform -t relevancy_sampler -- \
  --algorithm entropy \
  --subset_name train \
  --sample_name sample \
  --unsampled_name unsampled \
  --sampling_method topk -k 20
```

`random_sampler`#

Sampler that keeps no more than required number of items in the dataset.

Notes:

Items are selected uniformly (tries to keep original item distribution by subsets)
Requesting a sample larger than the number of all images will return all images

Usage:

random_sampler [-h] -k COUNT [-s SUBSET] [--seed SEED]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-k, --count (int) - Maximum number of items to sample
-s, --subset (str) - Limit changes to this subset (default: affect all dataset)
--seed (int) - Initial value for random number generator

Examples:

Select subset of 20 images randomly

datum transform -t random_sampler -- -k 20

Select subset of 20 images, modify only train subset

datum transform -t random_sampler -- -k 20 -s train

`random_label_sampler`#

Sampler that keeps at least the required number of annotations of each class in the dataset for each subset separately.

Consider using the “stats” command to get class distribution in the dataset.

Notes:

Items can contain annotations of several selected classes (e.g. 3 bounding boxes per image). The number of annotations in the resulting dataset varies between max(class counts) and sum(class counts)
If the input dataset does not has enough class annotations, the result will contain only what is available
Items are selected uniformly
For reasons above, the resulting class distribution in the dataset may not be the same as requested
The resulting dataset will only keep annotations for classes with specified count > 0

Usage:

label_random_sampler [-h] -k COUNT [-l LABEL_COUNTS] [--seed SEED]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-k, --count (int) - Minimum number of annotations of each class
-l, --label (str; repeatable) - Minimum number of annotations of a specific class. Overrides the -k/--count setting for the class. The format is <label_name>:<count>
--seed (int) - Initial value for random number generator

Examples:

Select a dataset with at least 10 images of each class
```
datum transform -t label_random_sampler -- -k 10
```

Select a dataset with at least 20 cat images, 5 dog, 0 car and 10 of each unmentioned class

datum transform -t label_random_sampler -- \
  -l cat:20 \ # keep 20 images with cats
  -l dog:5 \ # keep 5 images with dogs
  -l car:0 \ # remove car annotations
  -k 10 # for remaining classes

`resize`#

Resizes images and annotations in the dataset to the specified size. Supports upscaling, downscaling and mixed variants.

Usage:

resize [-h] [-dw WIDTH] [-dh HEIGHT]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-dw, --width (int) - Destination image width
-dh, --height (int) - Destination image height

Examples:

Resize all images to 256x256 size

datum transform -t resize -- -dw 256 -dh 256

`remove_images`#

Removes specific dataset items by their ids.

Usage:

remove_images [-h] [--id IDs]

Optional arguments:

-h, --help (flag) - Show this help message and exit
--id (str) - Item id to remove. Id is ‘:’ pair (repeatable)

Examples:

Remove specific images from the dataset

datum transform -t remove_images -- --id 'image1:train' --id 'image2:test'

`remove_annotations`#

Allows to remove annotations on specific dataset items.

Can be useful to clean the dataset from broken or unnecessary annotations.

Usage:

remove_annotations [-h] [--id IDs]

Optional arguments:

-h, --help (flag) - Show this help message and exit
--id (str) - Item id to clean from annotations. Id is ‘:’ pair. If not specified, removes all annotations (repeatable)

Examples:

Remove annotations from specific items in the dataset

datum transform -t remove_annotations -- --id 'image1:train' --id 'image2:test'

`remove_attributes`#

Allows to remove item and annotation attributes in a dataset.

Can be useful to clean the dataset from broken or unnecessary attributes.

Usage:

remove_attributes [-h] [--id IDs] [--attr ATTRIBUTE_NAME]

Optional arguments:

-h, --help (flag) - Show this help message and exit
--id (str) - Image id to clean from annotations. Id is ‘:’ pair. If not specified, affects all items and annotations (repeatable)
-a, --attr (flag) - Attribute name to be removed. If not specified, removes all attributes (repeatable)

Examples:

Remove the is_crowd attribute from dataset

datum transform -t remove_attributes -- \
  --attr 'is_crowd'

Remove the occluded attribute from annotations of the 2010_001705 item in the train subset

datum transform -t remove_attributes -- \
  --id '2010_001705:train' --attr 'occluded'

`astype_annotations`#

Enables the conversion of annotation types for the categories and individual items within a dataset. This transform only supports tabular datasets. If you want to change annotation types in datasets of other types, please use a different transform.

Based on default setting it transforms the annotation types, changing them to ‘Label’ if they are categorical, and to ‘Caption’ if they are of type string, float, or integer. If you specifically set mapping, change annotation types based on the mapping.

Usage:

astype_annotations [-h] [--mapping MAPPING]

Optional arguments:

-h, --help (flag) - Show this help message and exit
--mapping (str) - Annotations type in the form of: ‘:’ (repeatable)

Examples:

Convert type of title and rating annotation

datum transform -t astype_annotations -- \
  --mapping 'title:text,rating:label'

`random_split`#

Joins all subsets into one and splits the result into few parts. It is expected that item ids are unique and subset ratios sum up to 1.

Usage:

random_split [-h] [-s SPLITS] [--seed SEED]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-s, --subset (str, repeatable) - Subsets in the form: ‘:’ (repeatable, default: {train: 0.67, test: 0.33})
--seed (int) - Random seed

Examples:

Split a dataset randomly to train and test subsets, ratio is 2:1

datum transform -t random_split -- --subset train:.67 --subset test:.33

`split`#

Splits a dataset for model training, using task information:

classification splits Splits dataset into subsets (train/val/test) in class-wise manner. Splits dataset images in the specified ratio, keeping the initial class distribution.
detection & segmentation splits Each image can have multiple object annotations - bbox, mask, polygon. Since an image shouldn’t be included in multiple subsets at the same time, and image annotations shouldn’t be split, in general, dataset annotations are unlikely to be split exactly in the specified ratio. This split tries to split dataset images as close as possible to the specified ratio, keeping the initial class distribution.
reidentification splits In this task, the test set should consist of images of unseen people or objects during the training phase. This function splits a dataset in the following way:

Splits the dataset into train + val and test sets based on person or object ID.
Splits test set into test-gallery and test-query sets in class-wise manner.
Splits the train + val set into train and val sets in the same way. The final subsets would be train, val, test-gallery and test-query.

Notes:

Each image is expected to have only one Annotation. Unlabeled or multi-labeled images will be split into subsets randomly.
If Labels also have attributes, also splits by attribute values.
If there is not enough images in some class or attributes group, the split ratio can’t be guaranteed.

In reidentification task,

Object ID can be described by Label, or by attribute (--attr parameter)
The splits of the test set are controlled by --query parameter Gallery ratio would be 1.0 - query.

Usage:

split [-h] [-t {classification,detection,segmentation,reid}]
      [-s SPLITS] [--query QUERY] [--attr ATTR_FOR_ID] [--seed SEED]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-t, --task (one of: classification, detection, segmentation, reid) - Dataset task (default: classification)
-s, --subset (str; repeatable) - Subsets in the form: ‘:’ (default: {train: 0.5, val: 0.2, test: 0.3})
--query (float) - Query ratio in the test set (default: 0.5)
--attr (str) - Attribute name representing the ID (default: use label)
--seed(int) - Random seed

Examples:

Split by ratio

datum transform -t split -- -t classification \
  --subset train:.5 --subset val:.2 --subset test:.3

datum transform -t split -- -t detection \
  --subset train:.5 --subset val:.2 --subset test:.3

datum transform -t split -- -t segmentation \
  --subset train:.5 --subset val:.2 --subset test:.3

datum transform -t split -- -t reid \
  --subset train:.5 --subset val:.2 --subset test:.3 --query .5

Use person_id attribute for splitting

datum transform -t split -- -t detection --attr person_id

`map_subsets`#

Renames subsets in the dataset.

Usage:

map_subsets [-h] [-s MAPPING]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-s, --subset (str; repeatable) - Subset mapping of the form: src:dst

`remap_labels`#

Changes labels in the dataset.

A label can be:

renamed (and joined with existing) - when --label <old_name>:<new_name> is specified
deleted - when --label <name>: is specified, or default action is delete and the label is not mentioned in the list. When a label is deleted, all the associated annotations are removed
kept unchanged - when --label <name>:<name> is specified, or default action is keep and the label is not mentioned in the list Annotations with no label are managed by the default action policy.

Usage:

remap_labels [-h] [-l MAPPING] [--default {keep,delete}]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-l, --label (str; repeatable) - Label in the form of: <src>:<dst>
--default (one of: keep, delete) - Action for unspecified labels (default: keep)

Examples:

Remove the person label (and corresponding annotations)

datum transform -t remap_labels -- -l person: --default keep

Rename person to pedestrian and human to pedestrian, join annotations that had different classes under the same class id for pedestrian, don’t touch other classes
```
datum transform -t remap_labels -- \
  -l person:pedestrian -l human:pedestrian --default keep
```

Rename person to car and cat to dog, keep bus, remove others

datum transform -t remap_labels -- \
  -l person:car -l bus:bus -l cat:dog --default delete

`project_labels`#

Changes the order of labels in the dataset from the existing to the desired one, removes unknown labels and adds new labels. Updates or removes the corresponding annotations.

Labels are matched by names (case dependent). Parent labels are only kept if they are present in the resulting set of labels. If new labels are added, and the dataset has mask colors defined, new labels will obtain generated colors.

Useful for merging similar datasets, whose labels need to be aligned.

Usage:

project_labels [-h] [-l DST_LABELS]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-l, --label (str; repeatable) - Label name (ordered)

Examples:

Set dataset labels to [person, cat, dog], remove others, add missing. Original labels (for example): cat, dog, elephant, human. New labels: person (added), cat (kept), dog (kept).
```
datum transform -t project_labels -- -l person -l cat -l dog
```

`shapes_to_boxes`#

Converts spatial annotations (masks, polygons, polylines, points) to enclosing bounding boxes.

Usage:

shapes_to_boxes [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

Examples:

Convert spatial annotations between each other

datum transform -t boxes_to_masks
datum transform -t masks_to_polygons
datum transform -t polygons_to_masks
datum transform -t shapes_to_boxes

`boxes_to_masks`#

Converts bounding boxes to masks.

Usage:

boxes_to_masks [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`polygons_to_masks`#

Converts polygons to masks.

Usage:

polygons_to_masks [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`masks_to_polygons`#

Converts masks to polygons.

Usage:

masks_to_polygons [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`anns_to_labels`#

Collects all labels from annotations (of all types) and transforms them into a set of annotations of type Label

Usage:

anns_to_labels [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`merge_instance_segments`#

Replaces instance masks and, optionally, polygons with a single mask. A group of annotations with the same group id is considered an “instance”. The largest annotation in the group is considered the group “head”, so the resulting mask takes properties from that annotation.

Usage:

merge_instance_segments [-h] [--include-polygons]

Optional arguments:

-h, --help (flag) - Show this help message and exit
--include-polygons (flag) - Include polygons

`crop_covered_segments`#

Sorts polygons and masks (“segments”) according to z_order, crops covered areas of underlying segments. If a segment is split into several independent parts by the segments above, produces the corresponding number of separate annotations joined into a group.

Usage:

crop_covered_segments [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`bbox_value_decrement`#

Subtracts one from the coordinates of bounding boxes

Usage:

bbox_values_decrement [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

`pseudo_labeling`#

Assigns pseudo-labels to items in a dataset based on their similarity to predefined labels. This class is useful for semi-supervised learning when dealing with missing or uncertain labels.

The process includes:

Similarity Computation: Uses hashing techniques to compute the similarity between items and predefined labels.
Pseudo-Label Assignment: Assigns the most similar label as a pseudo-label to each item.

Attributes:

extractor (IDataset) - Provides access to dataset items and their annotations.
labels (Optional[List[str]]) - List of predefined labels for pseudo-labeling. Defaults to all available labels if not provided.
explorer (Optional[Explorer]) - Computes hash keys for items and labels. If not provided, a new Explorer is created.

Usage:

pseudo_labeling [-h] [--labels LABELS]

Optional arguments:
- `-h`, `--help` (flag) - Show this help message and exit
- `--labels` (str) - Comma-separated list of label names for pseudo-labeling

Examples:
- Assign pseudo-labels based on predefined labels
  ```console
  datum transform -t pseudo_labeling -- --labels 'label1,label2'

`correct`#

Correct the dataset from a validation report

Usage:

correct [-h] [-r REPORT_PATH]

Optional arguments:

-h, --help (flag) - Show this help message and exit
-r, --reports (str) - A validation report from a ‘validate’ CLI (default=validation_reports.json)

`clean`#

Refines and preprocesses media items in a dataset, focusing on string, numeric, and categorical data. This transform is designed to clean and improve the quality of the data, making it more suitable for analysis and modeling.

The cleaning process includes:

String Data: Removes unnecessary characters using NLP techniques.
Numeric Data: Identifies and handles outliers and missing values.
Categorical Data: Cleans and refines categorical information.

Usage:

clean [-h]

Optional arguments:

-h, --help (flag) - Show this help message and exit

Examples:

Clean and preprocess dataset items
```
datum transform -t clean
```

Transform#

Transform Dataset#

Built-in transforms#

rename#

id_from_image_name#

reindex#

sort#

ndr#

relevancy_sampler#

random_sampler#

random_label_sampler#

resize#

remove_images#

remove_annotations#

remove_attributes#

astype_annotations#

random_split#

split#

map_subsets#

remap_labels#

project_labels#

shapes_to_boxes#

boxes_to_masks#

polygons_to_masks#

masks_to_polygons#

anns_to_labels#

merge_instance_segments#

crop_covered_segments#

bbox_value_decrement#

pseudo_labeling#

correct#

clean#

`rename`#

`id_from_image_name`#

`reindex`#

`sort`#

`ndr`#

`relevancy_sampler`#

`random_sampler`#

`random_label_sampler`#

`resize`#

`remove_images`#

`remove_annotations`#

`remove_attributes`#

`astype_annotations`#

`random_split`#

`split`#

`map_subsets`#

`remap_labels`#

`project_labels`#

`shapes_to_boxes`#

`boxes_to_masks`#

`polygons_to_masks`#

`masks_to_polygons`#

`anns_to_labels`#

`merge_instance_segments`#

`crop_covered_segments`#

`bbox_value_decrement`#

`pseudo_labeling`#

`correct`#

`clean`#