Command reference
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The command line is split into the separate commands and command contexts.
Contexts group multiple commands related to a specific topic, e.g.
project operations, data source operations etc. Almost all the commands
operate on projects, so the project context and commands without a context
are mostly the same. By default, commands look for a project in the current
directory. If the project you’re working on is located somewhere else, you
can pass the -p/--project <path> argument to the command.
Note: command behavior is subject to change, so this text might be
outdated,
always check the --help output of the specific command
Note: command parameters must be passed prior to the positional arguments.
Datumaro functionality is available with the datum command.
Usage:
datum [-h] [--version] [--loglevel LOGLEVEL] [command] [command args]
Parameters:
--loglevel (string) - Logging level, one of
debug, info, warning, error, critical (default: info)--version - Print the version number and exit.-h, --help - Print the help message and exit.
1 - Checkout
This command allows to restore a specific project revision in the project
tree or to restore separate revisions of sources. A revision can be a commit
hash, branch, tag, or any relative reference in the Git format.
This command has multiple forms:
1) datum checkout <revision>
2) datum checkout [--] <source1> ...
3) datum checkout <revision> [--] <source1> <source2> ...
1 - Restores a revision and all the corresponding sources in the
working directory. If there are conflicts between modified files in the
working directory and the target revision, an error is raised, unless
--force is used.
2, 3 - Restores only selected sources from the specified revision.
The current revision is used, when not set.
“–” can be used to separate source names and revisions:
datum checkout name - will look for revision “name”datum checkout -- name - will look for source “name” in the current
revision
Usage:
datum checkout [-h] [-f] [-p PROJECT_DIR] [rev] [--] [sources [sources ...]]
Parameters:
--force - Allows to overwrite unsaved changes in case of conflicts-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Examples:
-
Restore the previous revision:
datum checkout HEAD~1
-
Restore the saved version of a source in the working tree
datum checkout -- source-1
-
Restore a previous version of a source
datum checkout 33fbfbe my-source
2 - Commit
This command allows to fix the current state of a project and
create a new revision from the working tree.
By default, this command checks sources in the working tree for
changes. If there are unknown changes found, an error will be raised,
unless --allow-foreign is used. If such changes are committed,
the source will only be available for reproduction from the project
cache, because Datumaro will not know how to repeat them.
The command will add the sources into the project cache. If you only
need to record revision metadata, you can use the --no-cache parameter.
This can be useful if you want to save disk space and/or have a backup copy
of datasets used in the project.
If there are no changes found, the command will stop. To allow empty
commits, use --allow-empty.
Usage:
datum commit [-h] -m MESSAGE [--allow-empty] [--allow-foreign]
[--no-cache] [-p PROJECT_DIR]
Parameters:
--allow-empty - Allow commits with no changes--allow-foreign - Allow commits with changes made not by Datumaro--no-cache - Don’t put committed datasets into cache, save only metadata-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example:
datum create
datum import -f coco <path/to/coco/>
datum commit -m "Added COCO"
3 - Convert datasets
This command allows to convert a dataset from one format to another.
The command is a usability alias for create,
add and export and just provides
a simpler way to obtain the same results in simple cases. A list of supported
formats can be found in the --help output of this command.
Usage:
datum convert [-h] [-i SOURCE] [-if INPUT_FORMAT] -f OUTPUT_FORMAT
[-o DST_DIR] [--overwrite] [-e FILTER] [--filter-mode FILTER_MODE]
[-- EXTRA_EXPORT_ARGS]
Parameters:
-i, --input-path (string) - Input dataset path. The current directory is
used by default.-if, --input-format (string) - Input dataset format. Will try to detect,
if not specified.-f, --output-format (string) - Output format-o, --output-dir (string) - Output directory. By default, a subdirectory
in the current directory is used.--overwrite - Allows overwriting existing files in the output directory,
when it is not empty.-e, --filter (string) - XML XPath filter expression for dataset items--filter-mode (string) - The filtering mode. Default is the i mode.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.-- <extra export args> - Additional arguments for the format writer
(use -- -h for help). Must be specified after the main command arguments.
Example: convert a VOC-like dataset to a COCO-like one:
datum convert --input-format voc --input-path <path/to/voc/> \
--output-format coco \
-- --save-images
4 - Create project
The command creates an empty project. A project is required for the most of
Datumaro functionality.
By default, the project is created in the current directory. To specify
another output directory, pass the -o/--output-dir parameter. If output
already directory contains a Datumaro project, an error is raised, unless
--overwrite is used.
Usage:
datum create [-h] [-o DST_DIR] [--overwrite]
Parameters:
-o, --output-dir (string) - Allows to specify an output directory.
The current directory is used by default.--overwrite - Allows to overwrite existing project files in the output
directory. Any other files are not touched.-h, --help - Print the help message and exit.
Examples:
Example: create an empty project in the my_dataset directory
datum create -o my_dataset/
Example: create a new empty project in the current directory, remove the
existing one
datum create
...
datum create --overwrite
5 - Detect dataset format
This command attempts to detect the format of a dataset in a directory.
Currently, only local directories are supported.
The detection result may be one of:
- a single format being detected;
- no formats being detected (if the dataset doesn’t match any known format);
- multiple formats being detected (if the dataset is ambiguous).
The command outputs this result in a human-readable form and
optionally as a machine-readable JSON report (see --json-report).
The format of the machine-readable report is as follows:
{
"detected_formats": [
"detected-format-name-1", "detected-format-name-2", ...
],
"rejected_formats": {
"rejected-format-name-1": {
"reason": <reason-code>,
"message": "line 1\nline 2\n...\nline N"
},
"rejected-format-name-2": ...,
...
}
}
The <reason-code> can be one of:
-
"detection_unsupported": the corresponding format does not support
detection.
-
"insufficient_confidence": the dataset matched the corresponding format,
but it matched at least one other format better.
-
"unmet_requirements": the dataset didn’t meet at least one requirement
of the corresponding format.
Other reason codes may be defined in the future.
Usage:
datum detect-format [-h] [-p PROJECT_DIR] [--show-rejections]
[--json-report JSON_REPORT]
url
Parameters:
<url> - Path to the dataset to analyse.-h, --help - Print the help message and exit.-p, --project (string) - Directory of the project to use as the context
(default: current directory). The project might contain local plugins with
custom formats, which will be used for detection.--show-rejections - Describe why each supported format that wasn’t
detected was rejected. This only affects the human-readable output; the
machine-readable report always includes rejection information.--json-report (string) - Path to which to save a JSON report describing
detected and rejected formats. By default, no report is saved.
Example: detect the format of a dataset in a given directory,
showing rejection information:
datum detect-format --show-rejections path/to/dataset
6 - Compare datasets
The command compares two datasets and saves the results in the
specified directory. The current project is considered to be
“ground truth”.
Datasets can be compared using different methods:
equality - Annotations are compared to be equaldistance - A distance metric is used
This command has multiple forms:
1) datum diff <revpath>
2) datum diff <revpath> <revpath>
1 - Compares the current project’s main target (project)
in the working tree with the specified dataset.
2 - Compares two specified datasets.
<revpath> - a dataset path or a revision path.
Usage:
datum diff [-h] [-o DST_DIR] [-m METHOD] [--overwrite] [-p PROJECT_DIR]
[--iou-thresh IOU_THRESH] [-f FORMAT]
[-iia IGNORE_ITEM_ATTR] [-ia IGNORE_ATTR] [-if IGNORE_FIELD]
[--match-images] [--all]
first_target [second_target]
Parameters:
-
<target> (string) - Target dataset revpaths
-
-m, --method (string) - Comparison method.
-
-o, --output-dir (string) - Output directory. By default, a new directory
is created in the current directory.
-
--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.
-
Distance comparison options:
--iou-thresh (number) - The IoU threshold for spatial annotations
(default is 0.5).-f, --format (string) - Output format, one of simple
(text files and images) and tensorboard (a TB log directory)
-
Equality comparison options:
-iia, --ignore-item-attr (string) - Ignore an item attribute (repeatable)-ia, --ignore-attr (string) - Ignore an annotation attribute (repeatable)-if, --ignore-field (string) - Ignore an annotation field (repeatable)
Default is id and group--match-images - Match dataset items by image pixels instead of ids--all - Include matches in the output. By default, only differences are
printed.
Examples:
-
Compare two projects by distance, match boxes if IoU > 0.7,
save results to TensorBoard:
datum diff other/project -o diff/ -f tensorboard --iou-thresh 0.7
-
Compare two projects for equality, exclude annotation groups
and the is_crowd attribute from comparison:
datum diff other/project/ -if group -ia is_crowd
-
Compare two datasets, specify formats:
datum diff path/to/dataset1:voc path/to/dataset2:coco
-
Compare the current working tree and a dataset:
datum diff path/to/dataset2:coco
-
Compare a source from a previous revision and a dataset:
datum diff HEAD~2:source-2 path/to/dataset2:yolo
-
Compare a dataset with model inference
datum create
datum import <...>
datum model add mymodel <...>
datum transform <...> -o inference
datum diff inference -o diff
7 - Download datasets
This command downloads a publicly available dataset and saves it to a local
directory.
In terms of syntax, this command is similar to convert,
but instead of taking a local directory as the source, it takes a dataset ID.
A list of supported datasets and output formats can be found in the --help
output of this command.
Currently, the only source of datasets is the TensorFlow Datasets library.
Therefore, to use this command you must install TensorFlow & TFDS, which you can
do as follows:
pip install datumaro[tf,tfds]
To use a proxy for downloading, configure it with the conventional
curl environment variables.
Usage:
datum download [-h] -i DATASET_ID [-f OUTPUT_FORMAT] [-o DST_DIR] [--overwrite]
[-- EXTRA_EXPORT_ARGS]
Parameters:
-h, --help - Print the help message and exit.-i, --dataset-id (string) - ID of the dataset to download.-f, --output-format (string) - Output format. By default, the format
of the original dataset is used.-o, --output-dir (string) - Output directory. By default, a subdirectory
in the current directory is used.--overwrite - Allows overwriting existing files in the output directory,
when it is not empty.-- <extra export args> - Additional arguments for the format writer
(use -- -h for help). Must be specified after the main command arguments.
Example: download the MNIST dataset, saving it in the ImageNet text format:
datum download -i tfds:mnist -f imagenet_txt -- --save-images
8 - Run model inference explanation (explain)
Runs an explainable AI algorithm for a model.
This tool is supposed to help an AI developer to debug a model and a dataset.
Basically, it executes model inference and tries to find relation between
inputs and outputs of the trained model, i.e. determine decision boundaries
and belief intervals for the classifier.
Currently, the only available algorithm is RISE (article),
which runs model a single time and then re-runs a model multiple times on
each image to produce a heatmap of activations for each output of the
first inference. Each time a part of the input image is masked. As a result,
we obtain a number heatmaps, which show, how specific image pixels affected
the inference result. This algorithm doesn’t require any special information
about the model, but it requires the model to return all the outputs and
confidences. The original algorithm supports only classification scenario,
but Datumaro extends it for detection models.
The following use cases available:
- RISE for classification
- RISE for object detection
Usage:
datum explain [-h] -m MODEL [-o SAVE_DIR] [-p PROJECT_DIR]
[target] {rise} [RISE_ARGS]
Parameters:
-
<target> (string) - Target
dataset revpath.By default,
uses the whole current project. An image path can be specified instead.
<image path> - a path to the file.
<revpath> - a dataset path or a revision path.
-
<method> (string) - The algorithm to use. Currently, only rise
is supported.
-
-m, --model (string) - The model to use for inference
-
-o, --output-dir (string) - Directory to save results to
(default: display only)
-
-p, --project (string) - Directory of the project to operate on
(default: current directory).
-
-h, --help - Print the help message and exit.
-
RISE options:
-s, --max-samples (number) - Number of algorithm model runs per image
(default: mask size ^ 2).--mw, --mask-width (number) - Mask width in pixels (default: 7)--mh, --mask-height (number) - Mask height in pixels (default: 7)--prob (number) - Mask pixel inclusion probability, controls
mask density (default: 0.5)--iou, --iou-thresh (number) - IoU match threshold for detections
(default: 0.9)--nms, --nms-iou-thresh (number) - IoU match threshold for detections
for non-maxima suppression (default: no NMS)--conf, --det-conf-thresh (number) - Confidence threshold for
detections (default: include all)-b, --batch-size (number) - Batch size for inference (default: 1)--display - Visualize results during computations
Examples:
-
Run RISE on an image, display results:
datum explain path/to/image.jpg -m mymodel rise --max-samples 50
-
Run RISE on a source revision:
datum explain HEAD~1:source-1 -m model rise
-
Run inference explanation on a single image with online visualization
datum create <...>
datum model add mymodel <...>
datum explain -t image.png -m mymodel \
rise --max-samples 1000 --display
Note: this algorithm requires the model to return
all (or a reasonable amount) the outputs and confidences unfiltered,
i.e. all the Label annotations for classification models and
all the Bboxes for detection models.
You can find examples of the expected model outputs in tests/test_RISE.py
For OpenVINO models the output processing script would look like this:
Classification scenario:
import datumaro as dm
from datumaro.util.annotation_util import softmax
def process_outputs(inputs, outputs):
# inputs = model input, array or images, shape = (N, C, H, W)
# outputs = model output, logits, shape = (N, n_classes)
# results = conversion result, [ [ Annotation, ... ], ... ]
results = []
for output in outputs:
confs = softmax(output[0])
for label, conf in enumerate(confs):
results.append(dm.Label(int(label)), attributes={'score': float(conf)})
return results
Object Detection scenario:
import datumaro as dm
# return a significant number of output boxes to make multiple runs
# statistically correct and meaningful
max_det = 1000
def process_outputs(inputs, outputs):
# inputs = model input, array or images, shape = (N, C, H, W)
# outputs = model output, shape = (N, 1, K, 7)
# results = conversion result, [ [ Annotation, ... ], ... ]
results = []
for input, output in zip(inputs, outputs):
input_height, input_width = input.shape[:2]
detections = output[0]
image_results = []
for det in detections:
label = int(det[1])
conf = float(det[2])
x = max(int(det[3] * input_width), 0)
y = max(int(det[4] * input_height), 0)
w = min(int(det[5] * input_width - x), input_width)
h = min(int(det[6] * input_height - y), input_height)
image_results.append(dm.Bbox(x, y, w, h,
label=label, attributes={'score': conf} ))
results.append(image_results[:max_det])
return results
9 - Export Datasets
This command exports a project or a source as a dataset in some format.
Check supported formats for more info
about format specifications, supported options and other details.
The list of formats can be extended by custom plugins, check extending tips
for information on this topic.
Available formats are listed in the command help output.
Dataset format writers support additional export options. To pass
such options, use the -- separator after the main command arguments.
The usage information can be printed with datum import -f <format> -- --help.
Common export options:
- Most formats (where applicable) support the
--save-images option, which
allows to export dataset images along with annotations. The option is
disabled be default.
- If
--save-images is used, the image-ext option can be passed to
specify the output image file extension (.jpg, .png etc.). By default,
tries to Datumaro keep the original image extension. This option
allows to convert all the images from one format into another.
This command allows to use the -f/--filter parameter to select dataset
elements needed for exporting. Read the filter command
description for more info about this functionality.
The command can only be applied to a project build target, a stage
or the combined project target, in which case all the targets will
be affected.
Usage:
datum export [-h] [-e FILTER] [--filter-mode FILTER_MODE] [-o DST_DIR]
[--overwrite] [-p PROJECT_DIR] -f FORMAT [target] [-- EXTRA_FORMAT_ARGS]
Parameters:
<target> (string) - A project build target to be exported.
By default, all project targets are affected.-f, --format (string) - Output format.-e, --filter (string) - XML XPath filter expression for dataset items--filter-mode (string) - The filtering mode. Default is the i mode.-o, --output-dir (string) - Output directory. By default, a subdirectory
in the current directory is used.--overwrite - Allows overwriting existing files in the output directory,
when it 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 format args> - Additional arguments for the format writer
(use -- -h for help). Must be specified after the main command arguments.
Example: save a project as a VOC-like dataset, include images, convert
images to PNG from other formats.
datum export \
-p test_project \
-o test_project-export \
-f voc \
-- --save-images --image-ext='.png'
10 - Filter datasets
This command allows to extract a sub-dataset from a dataset. The new dataset
includes only items satisfying some condition. The XML XPath
is used as a query format.
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.
There are several filtering modes available (the -m/--mode parameter).
Supported modes:
i, itemsa, annotationsi+a, a+i, items+annotations, annotations+items
When filtering annotations, use the items+annotations
mode to point that annotation-less dataset items should be
removed, otherwise they will be kept in the resulting dataset.
To select an annotation, write an XPath that returns annotation
elements (see examples).
Item representations can be printed with the --dry-run parameter:
<item>
<id>290768</id>
<subset>minival2014</subset>
<image>
<width>612</width>
<height>612</height>
<depth>3</depth>
</image>
<annotation>
<id>80154</id>
<type>bbox</type>
<label_id>39</label_id>
<x>264.59</x>
<y>150.25</y>
<w>11.19</w>
<h>42.31</h>
<area>473.87</area>
</annotation>
<annotation>
<id>669839</id>
<type>bbox</type>
<label_id>41</label_id>
<x>163.58</x>
<y>191.75</y>
<w>76.98</w>
<h>73.63</h>
<area>5668.77</area>
</annotation>
...
</item>
The command can only be applied to a project build target, a stage or the
combined project target, in which case all the targets will be affected.
A build tree stage will be added if --stage is enabled, and the resulting
dataset(-s) will be saved if --apply is enabled.
Usage:
datum filter [-h] [-e FILTER] [-m MODE] [--dry-run] [--stage STAGE]
[--apply APPLY] [-o DST_DIR] [--overwrite] [-p PROJECT_DIR] [target]
Parameters:
<target> (string) - Target
dataset revpath.
By default, filters all targets of the current project.-e, --filter (string) - XML XPath filter expression for dataset items-m, --mode (string) - The filtering mode. Default is the i mode.--dry-run - Print XML representations of the filtered dataset and exit.--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.
Example: extract a dataset with images with width < height
datum filter \
-p test_project \
-e '/item[image/width < image/height]'
Example: extract a dataset with images of the train subset
datum filter \
-p test_project \
-e '/item[subset="train"]'
Example: extract a dataset with only large annotations of the cat class and
any non-persons
datum filter \
-p test_project \
--mode annotations \
-e '/item/annotation[(label="cat" and area > 99.5) or label!="person"]'
Example: extract a dataset with non-occluded annotations, remove empty images.
Use data only from the “s1” source of the project.
datum create
datum import --format voc -i <path/to/dataset1/> --name s1
datum import --format voc -i <path/to/dataset2/> --name s2
datum filter s1 \
-m i+a -e '/item/annotation[occluded="False"]'
11 - Print dataset info
This command outputs high level dataset information such as sample count,
categories and subsets.
Usage:
datum info [-h] [--all] [-p PROJECT_DIR] [revpath]
Parameters:
<target> (string) - Target dataset revpath.
By default, prints info about the joined project dataset.--all - Print all the information: do not fold long lists of labels etc.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Examples:
Sample output:
length: 5000
categories: label
label:
count: 80
labels: person, bicycle, car, motorcycle (and 76 more)
subsets: minival2014
'minival2014':
length: 5000
categories: label
label:
count: 80
labels: person, bicycle, car, motorcycle (and 76 more)
12 - Log
This command prints the history of the current project revision.
Prints lines in the following format:
<short commit hash> <commit message>
Usage:
datum log [-h] [-n MAX_COUNT] [-p PROJECT_DIR]
Parameters:
-n, --max-count (number, default: 10) - The maximum number of
previous revisions in the output-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example output:
affbh33 Added COCO dataset
eeffa35 Added VOC dataset
13 - Merge Datasets
Consider the following task: there is a set of images (the original dataset)
we want to annotate. Suppose we did this manually and/or automated it
using models, and now we have few sets of annotations for the same images.
We want to merge them and produce a single set of high-precision annotations.
Another use case: there are few datasets with different sets of images
and labels, which we need to combine in a single dataset. If the labels
were the same, we could just join the datasets. But in this case we need
to merge labels and adjust the annotations in the resulting dataset.
In Datumaro, it can be done with the merge command. This command merges 2
or more datasets and checks annotations for errors.
In simple cases, when dataset images do not intersect and new
labels are not added, the recommended way of merging is using
the patch command.
It will offer better performance and provide the same results.
Datasets are merged by items, and item annotations are merged by finding the
unique ones across datasets. Annotations are matched between matching dataset
items by distance. Spatial annotations are compared by the applicable distance
measure (IoU, OKS, PDJ etc.), labels and annotation attributes are selected
by voting. Each set of matching annotations produces a single annotation in
the resulting dataset. The score (a number in the range [0; 1]) attribute
indicates the agreement between different sources in the produced annotation.
The working time of the function can be estimated as
O( (summary dataset length) * (dataset count) ^ 2 * (item annotations) ^ 2 )
This command also allows to merge datasets with different, or partially
overlapping sets of labels (which is impossible by simple joining).
During the process, some merge conflicts can appear. For example,
it can be mismatching dataset images having the same ids, label voting
can be unsuccessful if quorum is not reached (the --quorum parameter),
bboxes may be too close (the -iou parameter) etc. Found merge
conflicts, missing items or annotations, and other errors are saved into
an output .json file.
In Datumaro, annotations can be grouped. It can be useful to represent
different parts of a single object - for example, it can be different parts
of a human body, parts of a vehicle etc. This command allows to check
annotation groups for completeness with the -g/--groups option. If used,
this parameter must specify a list of labels for annotations that must be
in the same group. It can be particularly useful to check if separate
keypoints are grouped and all the necessary object components in the same
group.
This command has multiple forms:
1) datum merge <revpath>
2) datum merge <revpath> <revpath> ...
<revpath> - either a dataset path or a revision path.
1 - Merges the current project’s main target (“project”)
in the working tree with the specified dataset.
2 - Merges the specified datasets.
Note that the current project is not included in the list of merged
sources automatically.
The command supports passing extra exporting options for the output
dataset. The format can be specified with the -f/--format option.
Extra options should be passed after the main arguments
and after the -- separator. Particularly, this is useful to include
images in the output dataset with --save-images.
Usage:
datum merge [-h] [-iou IOU_THRESH] [-oconf OUTPUT_CONF_THRESH]
[--quorum QUORUM] [-g GROUPS] [-o DST_DIR] [--overwrite]
[-p PROJECT_DIR] [-f FORMAT]
target [target ...] [-- EXTRA_FORMAT_ARGS]
Parameters:
<target> (string) - Target dataset revpaths (repeatable)-iou, --iou-thresh (number) - IoU matching threshold for spatial
annotations (both maximum inter-cluster and pairwise). Default is 0.25.--quorum (number) - Minimum count of votes for a label or attribute
to be counted. Default is 0.-g, --groups (string) - A comma-separated list of label names in
annotation groups to check. The ? postfix can be added to a label to
make it optional in the group (repeatable)-oconf, --output-conf-thresh (number) - Confidence threshold for output
annotations to be included in the resulting dataset. Default is 0.-o, --output-dir (string) - Output directory. By default, a new directory
is created in the current directory.--overwrite - Allows to overwrite existing files in the output directory,
when it is specified and is not empty.-f, --format (string) - Output format. The default format is datumaro.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.-- <extra format args> - Additional arguments for the format writer
(use -- -h for help). Must be specified after the main command arguments.
Examples:
Merge 4 (partially-)intersecting projects,
- consider voting successful when there are no less than 3 same votes
- consider shapes intersecting when IoU >= 0.6
- check annotation groups to have
person, hand, head and foot
(? is used for optional parts)
datum merge project1/ project2/ project3/ project4/ \
--quorum 3 \
-iou 0.6 \
--groups 'person,hand?,head,foot?'
Merge images and annotations from 2 datasets in COCO format:
datum merge dataset1/:image_dir dataset2/:coco dataset3/:coco
Check groups of the merged dataset for consistency:
look for groups consisting of person, hand head, foot
datum merge project1/ project2/ -g 'person,hand?,head,foot?'
Merge two datasets, specify formats:
datum merge path/to/dataset1:voc path/to/dataset2:coco
Merge the current working tree and a dataset:
datum merge path/to/dataset2:coco
Merge a source from a previous revision and a dataset:
datum merge HEAD~2:source-2 path/to/dataset2:yolo
Merge datasets and save in different format:
datum merge -f voc dataset1/:yolo path2/:coco -- --save-images
14 - Models
Register model
Datumaro can execute deep learning models in various frameworks. Check
the plugins section
for more info.
Supported frameworks:
- OpenVINO
- Custom models via custom
launchers
Models need to be added to the Datumaro project first. It can be done with
the datum model add command.
Usage:
datum model add [-h] [-n NAME] -l LAUNCHER [--copy] [--no-check]
[-p PROJECT_DIR] [-- EXTRA_ARGS]
Parameters:
-l, --launcher (string) - Model launcher name--copy - Copy model data into project. By default, only the link is saved.--no-check - Don’t check the model can be loaded-n, --name (string) - Name of the new model (default: generate
automatically)-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.<extra args> - Additional arguments for the model launcher
(use -- -h for help). Must be specified after the main command arguments.
Example: register an OpenVINO model
A model consists of a graph description and weights. There is also a script
used to convert model outputs to internal data structures.
datum create
datum model add \
-n <model_name> -l openvino -- \
-d <path_to_xml> -w <path_to_bin> -i <path_to_interpretation_script>
Interpretation script for an OpenVINO detection model (convert.py):
You can find OpenVINO model interpreter samples in
datumaro/plugins/openvino/samples (instruction).
import datumaro as dm
max_det = 10
conf_thresh = 0.1
def process_outputs(inputs, outputs):
# inputs = model input, array or images, shape = (N, C, H, W)
# outputs = model output, shape = (N, 1, K, 7)
# results = conversion result, [ [ Annotation, ... ], ... ]
results = []
for input, output in zip(inputs, outputs):
input_height, input_width = input.shape[:2]
detections = output[0]
image_results = []
for det in detections:
label = int(det[1])
conf = float(det[2])
if conf <= conf_thresh:
continue
x = max(int(det[3] * input_width), 0)
y = max(int(det[4] * input_height), 0)
w = min(int(det[5] * input_width - x), input_width)
h = min(int(det[6] * input_height - y), input_height)
image_results.append(dm.Bbox(x, y, w, h,
label=label, attributes={'score': conf} ))
results.append(image_results[:max_det])
return results
def get_categories():
# Optionally, provide output categories - label map etc.
# Example:
label_categories = dm.LabelCategories()
label_categories.add('person')
label_categories.add('car')
return { dm.AnnotationType.label: label_categories }
Remove Models
To remove a model from a project, use the datum model remove command.
Usage:
datum model remove [-h] [-p PROJECT_DIR] name
Parameters:
<name> (string) - The name of the model to be removed-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example:
datum create
datum model add <...> -n model1
datum remove model1
Run Model
This command applies model to dataset images and produces a new dataset.
Usage:
Parameters:
<target> (string) - A project build target to be used.
By default, uses the combined project target.-m, --model (string) - Model name-o, --output-dir (string) - Output directory. By default, results will
be stored in an auto-generated directory in the current directory.--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.
Example: launch inference on a dataset
datum create
datum import <...>
datum model add mymodel <...>
datum model run -m mymodel -o inference
15 - Patch Datasets
Updates items of the first dataset with items from the second one.
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 along with the
--save-images export option (in-place updates fail by default
to prevent data loss).
Unlike the regular project data source joining,
the datasets are not required to have the same labels. The labels from
the “patch” dataset are projected onto the labels of the patched dataset,
so only the annotations with the matching labels are used, i.e.
all the annotations having unknown labels are ignored. Currently,
this command doesn’t allow to update the label information in the
patched dataset.
The command supports passing extra exporting options for the output
dataset. The extra options should be passed after the main arguments
and after the -- separator. Particularly, this is useful to include
images in the output dataset with --save-images.
This command can be applied to the current project targets or
arbitrary datasets outside a project. Note that if the target dataset
is read-only (e.g. if it is a project, stage or a cache entry),
the output directory must be provided.
Usage:
datum patch [-h] [-o DST_DIR] [--overwrite] [-p PROJECT_DIR]
target patch
[-- EXPORT_ARGS]
<revpath> - either a dataset path or a revision path.
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.
Parameters:
<target dataset> (string) - Target dataset revpath<patch dataset> (string) - Patch dataset revpath-o, --output-dir (string) - Output directory. By default, saves in-place--overwrite - Allows to overwrite existing files in the output directory,
when it is not empty.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.-- <export args> - Additional arguments for the format writer
(use -- -h for help). Must be specified after the main command arguments.
Examples:
- Update a VOC-like dataset with COCO-like annotations:
datum patch --overwrite dataset1/:voc dataset2/:coco -- --save-images
- Generate a patched dataset, based on a project:
datum patch -o patched_proj1/ proj1/ proj2/
- Update the “source1” source in the current project with a dataset:
datum patch -p proj/ --overwrite source1 path/to/dataset2:coco
- Generate a patched source from a previous revision and a dataset:
datum patch -o new_src2/ HEAD~2:source-2 path/to/dataset2:yolo
- Update a dataset in a custom format, described in a project plugin:
datum patch -p proj/ --overwrite dataset/:my_format dataset2/:coco
16 - Projects
Migrate project
Updates the project from an old version to the current one and saves the
resulting project in the output directory. Projects cannot be updated
inplace.
The command tries to map the old source configuration to the new one.
This can fail in some cases, so the command will exit with an error,
unless -f/--force is specified. With this flag, the command will
skip these errors an continue its work.
Usage:
datum project migrate [-h] -o DST_DIR [-f] [-p PROJECT_DIR] [--overwrite]
Parameters:
-o, --output-dir (string) - Output directory for the updated project-f, --force - Ignore source import errors (default: False)--overwrite - Overwrite existing files in the save directory.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Examples:
- Migrate a project from v1 to v2, save the new project in other dir:
datum project migrate -o <output/dir>
Print project info
Prints project configuration info such as available plugins, registered models,
imported sources and build tree.
Usage:
datum project info [-h] [-p PROJECT_DIR] [revision]
Parameters:
<revision> (string) - Target project revision. By default,
uses the working tree.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Examples:
Sample output:
Project:
location: /test_proj
Plugins:
extractors: ade20k2017, ade20k2020, camvid, cifar, cityscapes, coco, coco_captions, coco_image_info, coco_instances, coco_labels, coco_panoptic, coco_person_keypoints, coco_stuff, cvat, datumaro, icdar_text_localization, icdar_text_segmentation, icdar_word_recognition, image_dir, image_zip, imagenet, imagenet_txt, kitti, kitti_detection, kitti_raw, kitti_segmentation, label_me, lfw, market1501, mnist, mnist_csv, mot_seq, mots, mots_png, open_images, sly_pointcloud, tf_detection_api, vgg_face2, voc, voc_action, voc_classification, voc_detection, voc_layout, voc_segmentation, wider_face, yolo
converters: camvid, mot_seq_gt, coco_captions, coco, coco_image_info, coco_instances, coco_labels, coco_panoptic, coco_person_keypoints, coco_stuff, kitti, kitti_detection, kitti_segmentation, icdar_text_localization, icdar_text_segmentation, icdar_word_recognition, lfw, datumaro, open_images, image_zip, cifar, yolo, voc_action, voc_classification, voc, voc_detection, voc_layout, voc_segmentation, tf_detection_api, label_me, mnist, cityscapes, mnist_csv, kitti_raw, wider_face, vgg_face2, sly_pointcloud, mots_png, image_dir, imagenet_txt, market1501, imagenet, cvat
launchers:
Models:
Sources:
'source-2':
format: voc
url: /datasets/pascal/VOC2012
location: /test_proj/source-2/
options: {}
hash: 3eb282cdd7339d05b75bd932a1fd3201
stages:
'root':
type: source
hash: 3eb282cdd7339d05b75bd932a1fd3201
'source-3':
format: imagenet
url: /datasets/imagenet/ILSVRC2012_img_val/train
location: /test_proj/source-3/
options: {}
hash: e47804a3ec1a54c9b145e5f1007ec72f
stages:
'root':
type: source
hash: e47804a3ec1a54c9b145e5f1007ec72f
17 - Sources
These commands are specific for Data Sources. Read more about them here.
Import Dataset
Datasets can be added to a Datumaro project with the import command,
which adds a dataset link into the project and downloads (or copies)
the dataset. If you need to add a dataset already copied into the project,
use the add command.
Dataset format readers can provide some additional import options. To pass
such options, use the -- separator after the main command arguments.
The usage information can be printed with datum import -f <format> -- --help.
The list of currently available formats is listed in the command help output.
A dataset is imported by its URL. Currently, only local filesystem
paths are supported. The URL can be a file or a directory path
to a dataset. When the dataset is read, it is read as a whole.
However, many formats can have multiple subsets like train, val, test
etc. If you want to limit reading only to a specific subset, use
the -r/--path parameter. It can also be useful when subset files have
non-standard placement or names.
When a dataset is imported, the following things are done:
- URL is saved in the project config
- data in copied into the project
Each data source has a name assigned, which can be used in other commands. To
set a specific name, use the -n/--name parameter.
The dataset is added into the working tree of the project. A new commit
is not done automatically.
Usage:
datum import [-h] [-n NAME] -f FORMAT [-r PATH] [--no-check]
[-p PROJECT_DIR] url [-- EXTRA_FORMAT_ARGS]
Parameters:
<url> (string) - A file of directory path to the dataset.-f, --format (string) - Dataset format-r, --path (string) - A path relative to the source URL the data source.
Useful to specify a path to a subset, subtask, or a specific file in URL.--no-check - Don’t try to read the source after importing-n, --name (string) - Name of the new source (default: generate
automatically)-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.-- <extra format args> - Additional arguments for the format reader
(use -- -h for help). Must be specified after the main command arguments.
Example: create a project from images and annotations in different formats,
export as TFrecord for TF Detection API for model training
# 'default' is the name of the subset below
datum create
datum import -f coco_instances -r annotations/instances_default.json path/to/coco
datum import -f cvat <path/to/cvat/default.xml>
datum import -f voc_detection -r custom_subset_dir/default.txt <path/to/voc>
datum import -f datumaro <path/to/datumaro/default.json>
datum import -f image_dir <path/to/images/dir>
datum export -f tf_detection_api -- --save-images
Add Dataset
Existing datasets can be added to a Datumaro project with the add command.
The command adds a project-local directory as a data source in the project.
Unlike the import
command, it does not copy datasets and only works with local directories.
The source name is defined by the directory name.
Dataset format readers can provide some additional import options. To pass
such options, use the -- separator after the main command arguments.
The usage information can be printed with datum add -f <format> -- --help.
The list of currently available formats is listed in the command help output.
A dataset is imported as a directory. When the dataset is read, it is read
as a whole. However, many formats can have multiple subsets like train,
val, test etc. If you want to limit reading only to a specific subset,
use the -r/--path parameter. It can also be useful when subset files have
non-standard placement or names.
The dataset is added into the working tree of the project. A new commit
is not done automatically.
Usage:
datum add [-h] -f FORMAT [-r PATH] [--no-check]
[-p PROJECT_DIR] path [-- EXTRA_FORMAT_ARGS]
Parameters:
<url> (string) - A file of directory path to the dataset.-f, --format (string) - Dataset format-r, --path (string) - A path relative to the source URL the data source.
Useful to specify a path to a subset, subtask, or a specific file in URL.--no-check - Don’t try to read the source after importing-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.-- <extra format args> - Additional arguments for the format reader
(use -- -h for help). Must be specified after the main command arguments.
Example: create a project from images and annotations in different formats,
export in YOLO for model training
datum create
datum add -f coco -r annotations/instances_train.json dataset1/
datum add -f cvat dataset2/train.xml
datum export -f yolo -- --save-images
Example: add an existing dataset into a project, avoid data copying
To add a dataset, we need to have it inside the project directory:
proj/
├─ .datumaro/
├─ .dvc/
├─ my_coco/
│ └─ images/
│ ├─ image1.jpg
│ └─ ...
│ └─ annotations/
│ └─ coco_annotation.json
├─ .dvcignore
└─ .gitignore
datum create -o proj/
mv ~/my_coco/ proj/my_coco/ # move the dataset into the project directory
datum add -p proj/ -f coco proj/my_coco/
Remove Datasets
To remove a data source from a project, use the remove command.
Usage:
datum remove [-h] [--force] [--keep-data] [-p PROJECT_DIR] name [name ...]
Parameters:
<name> (string) - The name of the source to be removed (repeatable)-f, --force - Do not fail and stop on errors during removal--keep-data - Do not remove source data from the working directory, remove
only project metainfo.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example:
datum create
datum import -f voc -n src1 <path/to/dataset/>
datum remove src1
18 - Get Project Statistics
This command computes various project statistics, such as:
- image mean and std. dev.
- class and attribute balance
- mask pixel balance
- segment area distribution
Usage:
datum stats [-h] [-p PROJECT_DIR] [target]
Parameters:
<target> (string) - Target
source revpath.
By default, computes statistics of the merged dataset.-s, --subset (string) - Compute stats only for a specific subset--image-stats (bool) - Compute image mean and std (default: True)--ann-stats (bool) - Compute annotation statistics (default: True)-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example:
datum stats -p test_project
Sample output:
{
"annotations": {
"labels": {
"attributes": {
"gender": {
"count": 358,
"distribution": {
"female": [
149,
0.41620111731843573
],
"male": [
209,
0.5837988826815642
]
},
"values count": 2,
"values present": [
"female",
"male"
]
},
"view": {
"count": 340,
"distribution": {
"__undefined__": [
4,
0.011764705882352941
],
"front": [
54,
0.1588235294117647
],
"left": [
14,
0.041176470588235294
],
"rear": [
235,
0.6911764705882353
],
"right": [
33,
0.09705882352941177
]
},
"values count": 5,
"values present": [
"__undefined__",
"front",
"left",
"rear",
"right"
]
}
},
"count": 2038,
"distribution": {
"car": [
340,
0.16683022571148184
],
"cyclist": [
194,
0.09519136408243375
],
"head": [
354,
0.17369970559371933
],
"ignore": [
100,
0.04906771344455348
],
"left_hand": [
238,
0.11678115799803729
],
"person": [
358,
0.17566241413150147
],
"right_hand": [
77,
0.037782139352306184
],
"road_arrows": [
326,
0.15996074582924436
],
"traffic_sign": [
51,
0.025024533856722278
]
}
},
"segments": {
"area distribution": [
{
"count": 1318,
"max": 11425.1,
"min": 0.0,
"percent": 0.9627465303140978
},
{
"count": 1,
"max": 22850.2,
"min": 11425.1,
"percent": 0.0007304601899196494
},
{
"count": 0,
"max": 34275.3,
"min": 22850.2,
"percent": 0.0
},
{
"count": 0,
"max": 45700.4,
"min": 34275.3,
"percent": 0.0
},
{
"count": 0,
"max": 57125.5,
"min": 45700.4,
"percent": 0.0
},
{
"count": 0,
"max": 68550.6,
"min": 57125.5,
"percent": 0.0
},
{
"count": 0,
"max": 79975.7,
"min": 68550.6,
"percent": 0.0
},
{
"count": 0,
"max": 91400.8,
"min": 79975.7,
"percent": 0.0
},
{
"count": 0,
"max": 102825.90000000001,
"min": 91400.8,
"percent": 0.0
},
{
"count": 50,
"max": 114251.0,
"min": 102825.90000000001,
"percent": 0.036523009495982466
}
],
"avg. area": 5411.624543462382,
"pixel distribution": {
"car": [
13655,
0.0018431496518735067
],
"cyclist": [
939005,
0.12674674030446592
],
"head": [
0,
0.0
],
"ignore": [
5501200,
0.7425510702956085
],
"left_hand": [
0,
0.0
],
"person": [
954654,
0.12885903974805205
],
"right_hand": [
0,
0.0
],
"road_arrows": [
0,
0.0
],
"traffic_sign": [
0,
0.0
]
}
}
},
"annotations by type": {
"bbox": {
"count": 548
},
"caption": {
"count": 0
},
"label": {
"count": 0
},
"mask": {
"count": 0
},
"points": {
"count": 669
},
"polygon": {
"count": 821
},
"polyline": {
"count": 0
}
},
"annotations count": 2038,
"unannotated images": [
"img00051",
"img00052",
"img00053",
"img00054",
"img00055",
],
"unannotated images count": 5,
"dataset": {
"images count": 100,
"unique images count": 97,
"repeated images count": 3,
"repeated images": [
[["img00057", "default"], ["img00058", "default"]],
[["img00059", "default"], ["img00060", "default"]],
[["img00061", "default"], ["img00062", "default"]],
],
},
"subsets": {
"default": {
"images count": 100,
"image mean": [
107.06903686941979,
79.12831698580979,
52.95829558185416
],
"image std": [
49.40237673503467,
43.29600731496902,
35.47373007603151
],
}
},
}
19 - Status
This command prints the summary of the source changes between
the working tree of a project and its HEAD revision.
Prints lines in the following format:
<status> <source name>
The list of possible status values:
modified - the source data exists and it is changedforeign_modified - the source data exists and it is changed,
but Datumaro does not know about the way the differences were made.
If changes are committed, they will only be available for reproduction
from the project cache.added - the source was added in the working treeremoved - the source was removed from the working tree. This status won’t
be reported if just the source data is removed in the working tree.
In such situation the status will be missing.missing - the source data is removed from the working directory.
The source still can be restored from the project cache or reproduced.
Usage:
datum status [-h] [-p PROJECT_DIR]
Parameters:
-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.
Example output:
added source-1
modified source-2
foreign_modified source-3
removed source-4
missing source-5
20 - 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:
datum create <...>
datum import <...> -n source-1
datum transform -t rename source-1 -- -e '|^frame_||'
Basic dataset item manipulations:
Subset manipulations:
random_split - Splits dataset into subsets
randomlysplit - Splits dataset into subsets for classification,
detection, segmentation or re-identificationmap_subsets - Renames and removes subsets
Annotation manipulations:
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:
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_|\1|'
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|'
Renames items in the dataset using image file name (without extension).
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
Replaces dataset item IDs with sequential indices.
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit-s, --start (int) - Start value for item ids (default: 1)
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 randomlysimilarity - sample from removed data with ascending similarity score
Available undersampling policies (the -u parameter):
uniform - sample data with uniform distributioninverse - 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
Example: 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
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 datalowk - Return the k items with the lowest uncertainty datarandk - Return random k itemsmixk - Return a half using topk, and the other half using lowk methodrandtopk - 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
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
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
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
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'
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'
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'
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
Example:
Split a dataset randomly to train and test subsets, ratio is 2:1
datum transform -t random_split -- --subset train:.67 --subset test:.33
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
Example:
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
Example: use person_id attribute for splitting
datum transform -t split -- -t detection --attr person_id
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
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
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
Converts spatial annotations (masks, polygons, polylines, points)
to enclosing bounding boxes.
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
Example:
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
Converts bounding boxes to masks.
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
Converts polygons to masks.
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
Converts masks to polygons.
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
Collects all labels from annotations (of all types) and transforms
them into a set of annotations of type Label
Usage:
Optional arguments:
-h, --help (flag) - Show this help message and exit
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
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
Subtracts one from the coordinates of bounding boxes
Usage:
bbox_values_decrement [-h]
Optional arguments:
-h, --help (flag) - Show this help message and exit
21 - Utilities
Split video into frames
Splits a video into separate frames and saves them in a directory.
After the splitting, the images can be added into a project using
the import command and the image_dir format.
This command is useful for making a dataset from a video file.
Unlike direct video reading during model training, which can produce
different results if the system environment changes, this command
allows to split the video into frames and use them instead, making
the dataset reproducible and stable.
This command provides different options like setting the frame step
(the -s/--step option), file name pattern (-n/--name-pattern),
starting (-b/--start-frame) and finishing (-e/--end-frame) frame etc.
Note that this command is equivalent to the following commands:
datum create -o proj
datum import -p proj -f video_frames video.mp4 -- <params>
datum export -p proj -f image_dir -- <params>
Usage:
datum util split_video [-h] -i SRC_PATH [-o DST_DIR] [--overwrite]
[-n NAME_PATTERN] [-s STEP] [-b START_FRAME] [-e END_FRAME] [-x IMAGE_EXT]
Parameters:
-i, --input-path (string) - Path to the video file-o, --output-dir (string) - Output directory. By default, a subdirectory
in the current directory is used--overwrite - Allows overwriting existing files in the output directory,
when it is not empty-n, --name-pattern (string) - Name pattern for the produced
images (default: %06d)-s, --step (integer) - Frame step (default: 1)-b, --start-frame (integer) - Starting frame (default: 0)-e, --end-frame (integer) - Finishing frame (default: none)-x, --image-ext (string) Output image extension (default: .jpg)-h, --help - Print the help message and exit
Example: split a video into frames, use each 30-rd frame:
datum util split_video -i video.mp4 -o video.mp4-frames --step 30
Example: split a video into frames, save as ‘frame_xxxxxx.png’ files:
datum util split_video -i video.mp4 --image-ext=.png --name-pattern='frame_%%06d'
Example: split a video, add frames and annotations into dataset, export as YOLO:
datum util split_video -i video.avi -o video-frames
datum create -o proj
datum import -p proj -f image_dir video-frames
datum import -p proj -f coco_instances annotations.json
datum export -p proj -f yolo -- --save-images
22 - Validate Dataset
This command inspects annotations with respect to the task type
and stores the results in JSON file.
The task types supported are classification, detection, and
segmentation (the -t/--task-type parameter).
The validation result contains
annotation statistics based on the task typevalidation reports, such as
- items not having annotations
- items having undefined annotations
- imbalanced distribution in class/attributes
- too small or large values
summary
Usage:
datum validate [-h] -t TASK [-s SUBSET_NAME] [-p PROJECT_DIR]
[target] [-- EXTRA_ARGS]
Parameters:
<target> (string) - Target
dataset revpath.
By default, validates the current project.-t, --task-type (string) - Task type for validation-s, --subset (string) - Dataset subset to be validated-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 validation parameters. Should be passed
after the -- separator after the main command arguments:
-fs, --few-samples-thr (number) - The threshold for giving a warning
for minimum number of samples per class-ir, --imbalance-ratio-thr (number) - The threshold for giving
imbalance data warning-m, --far-from-mean-thr (number) - The threshold for giving
a warning that data is far from mean-dr, --dominance-ratio-thr (number) - The threshold for giving
a warning bounding box imbalance-k, --topk-bins (number) - The ratio of bins with the highest
number of data to total bins in the histogram
Example : give warning when imbalance ratio of data with classification task
over 40
datum validate -p prj/ -t classification -- -ir 40
Here is the list of validation items(a.k.a. anomaly types).
| Anomaly Type |
Description |
Task Type |
| MissingLabelCategories |
Metadata (ex. LabelCategories) should be defined |
common |
| MissingAnnotation |
No annotation found for an Item |
common |
| MissingAttribute |
An attribute key is missing for an Item |
common |
| MultiLabelAnnotations |
Item needs a single label |
classification |
| UndefinedLabel |
A label not defined in the metadata is found for an item |
common |
| UndefinedAttribute |
An attribute not defined in the metadata is found for an item |
common |
| LabelDefinedButNotFound |
A label is defined, but not found actually |
common |
| AttributeDefinedButNotFound |
An attribute is defined, but not found actually |
common |
| OnlyOneLabel |
The dataset consists of only label |
common |
| OnlyOneAttributeValue |
The dataset consists of only attribute value |
common |
| FewSamplesInLabel |
The number of samples in a label might be too low |
common |
| FewSamplesInAttribute |
The number of samples in an attribute might be too low |
common |
| ImbalancedLabels |
There is an imbalance in the label distribution |
common |
| ImbalancedAttribute |
There is an imbalance in the attribute distribution |
common |
| ImbalancedDistInLabel |
Values (ex. bbox width) are not evenly distributed for a label |
detection, segmentation |
| ImbalancedDistInAttribute |
Values (ex. bbox width) are not evenly distributed for an attribute |
detection, segmentation |
| NegativeLength |
The width or height of bounding box is negative |
detection |
| InvalidValue |
There’s invalid (ex. inf, nan) value for bounding box info. |
detection |
| FarFromLabelMean |
An annotation has an too small or large value than average for a label |
detection, segmentation |
| FarFromAttrMean |
An annotation has an too small or large value than average for an attribute |
detection, segmentation |
Validation Result Format:
{
'statistics': {
## common statistics
'label_distribution': {
'defined_labels': <dict>, # <label:str>: <count:int>
'undefined_labels': <dict>
# <label:str>: {
# 'count': <int>,
# 'items_with_undefined_label': [<item_key>, ]
# }
},
'attribute_distribution': {
'defined_attributes': <dict>,
# <label:str>: {
# <attribute:str>: {
# 'distribution': {<attr_value:str>: <count:int>, },
# 'items_missing_attribute': [<item_key>, ]
# }
# }
'undefined_attributes': <dict>
# <label:str>: {
# <attribute:str>: {
# 'distribution': {<attr_value:str>: <count:int>, },
# 'items_with_undefined_attr': [<item_key>, ]
# }
# }
},
'total_ann_count': <int>,
'items_missing_annotation': <list>, # [<item_key>, ]
## statistics for classification task
'items_with_multiple_labels': <list>, # [<item_key>, ]
## statistics for detection task
'items_with_invalid_value': <dict>,
# '<item_key>': {<ann_id:int>: [ <property:str>, ], }
# - properties: 'x', 'y', 'width', 'height',
# 'area(wxh)', 'ratio(w/h)', 'short', 'long'
# - 'short' is min(w,h) and 'long' is max(w,h).
'items_with_negative_length': <dict>,
# '<item_key>': { <ann_id:int>: { <'width'|'height'>: <value>, }, }
'bbox_distribution_in_label': <dict>, # <label:str>: <bbox_template>
'bbox_distribution_in_attribute': <dict>,
# <label:str>: {<attribute:str>: { <attr_value>: <bbox_template>, }, }
'bbox_distribution_in_dataset_item': <dict>,
# '<item_key>': <bbox count:int>
## statistics for segmentation task
'items_with_invalid_value': <dict>,
# '<item_key>': {<ann_id:int>: [ <property:str>, ], }
# - properties: 'area', 'width', 'height'
'mask_distribution_in_label': <dict>, # <label:str>: <mask_template>
'mask_distribution_in_attribute': <dict>,
# <label:str>: {
# <attribute:str>: { <attr_value>: <mask_template>, }
# }
'mask_distribution_in_dataset_item': <dict>,
# '<item_key>': <mask/polygon count: int>
},
'validation_reports': <list>, # [ <validation_error_format>, ]
# validation_error_format = {
# 'anomaly_type': <str>,
# 'description': <str>,
# 'severity': <str>, # 'warning' or 'error'
# 'item_id': <str>, # optional, when it is related to a DatasetItem
# 'subset': <str>, # optional, when it is related to a DatasetItem
# }
'summary': {
'errors': <count: int>,
'warnings': <count: int>
}
}
item_key is defined as,
item_key = (<DatasetItem.id:str>, <DatasetItem.subset:str>)
bbox_template and mask_template are defined as,
bbox_template = {
'width': <numerical_stat_template>,
'height': <numerical_stat_template>,
'area(wxh)': <numerical_stat_template>,
'ratio(w/h)': <numerical_stat_template>,
'short': <numerical_stat_template>, # short = min(w, h)
'long': <numerical_stat_template> # long = max(w, h)
}
mask_template = {
'area': <numerical_stat_template>,
'width': <numerical_stat_template>,
'height': <numerical_stat_template>
}
numerical_stat_template is defined as,
numerical_stat_template = {
'items_far_from_mean': <dict>,
# {'<item_key>': {<ann_id:int>: <value:float>, }, }
'mean': <float>,
'stddev': <float>,
'min': <float>,
'max': <float>,
'median': <float>,
'histogram': {
'bins': <list>, # [<float>, ]
'counts': <list>, # [<int>, ]
}
}