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Welcome to the documentation for the Dataset Management Framework (Datumaro).
The Datumaro is a free framework and CLI tool for building, transforming, and analyzing datasets. It is developed and used by Intel to build, transform, and analyze annotations and datasets in a large number of supported formats.
Our documentation provides information for AI researchers, developers, and teams, who are working with datasets and annotations.
To read about the design concept and features of Datumaro, go to the design section.
Optionally, create a virtual environment:
Install Datumaro package:
Read full installation instructions in the user manual.
There are several options available:
Datuaro as a standalone tool allows to do various dataset operations from the command line interface:
Datumaro can be used in custom scripts as a Python module. Used this way, it allows to use its features from an existing codebase, enabling dataset reading, exporting and iteration capabilities, simplifying integration of custom formats and providing high performance operations:
Check our developer manual for additional information.
Convert PASCAL VOC dataset to COCO format, keep only images with cat class
presented:
Convert only non-occluded annotations from a
CVAT project to TFrecord:
Annotate MS COCO dataset, extract image subset, re-annotate it in CVAT, update old dataset:
Annotate instance polygons in CVAT, export as masks in COCO:
Apply an OpenVINO detection model to some COCO-like dataset, then compare annotations with ground truth and visualize in TensorBoard:
Change colors in PASCAL VOC-like .png masks:
Create a custom COCO-like dataset:
Datumaro is:
Datumaro needs to be integrated with CVAT, extending CVAT UI capabilities regarding task and project operations. It should be capable of downloading and processing data from CVAT.
pip by nameDataset format support (reading, writing)
Dataset visualization (show)
Calculation of statistics for datasets
Dataset building
extract)merge)edit)Dataset comparison (diff)
Dataset and model debugging
explain)CVAT-integration features
Dataset publishing
Dataset building
Dataset and model debugging
explain)
Optionally, set up a virtual environment:
Install:
Read more about choosing between datumaro and datumaro[default]
here.
Datumaro has many plugins, which are responsible for dataset formats, model launchers and other optional components. If a plugin has dependencies, they can require additional installation. You can find the list of all the plugin dependencies in the plugins section.
Datumaro has the following installation options:
pip install datumaro - for core library functionalitypip install datumaro[default] - for normal CLI experienceIn restricted installation environments, where some dependencies are not available, or if you need only the core library functionality, you can install Datumaro without extra plugins.
In some cases, installing just the core library may be not enough,
because there can be limited options of installing graphical libraries
in the system (various Docker environments, servers etc). You can select
between using opencv-python and opencv-python-headless by setting the
DATUMARO_HEADLESS environment variable to 0 or 1 before installing
the package. It requires installation from sources (using --no-binary):
This option can’t be covered by extras due to Python packaging system limitations.
When installing directly from the repository, you can change the
installation branch with ...@<branch_name>. Also use --force-reinstall
parameter in this case. It can be useful for testing of unreleased
versions from GitHub pull requests.
As a standalone tool or a Python module:
As a Python library:
Basic concepts:
Project versioning concepts:
HEAD
revision is the currently selected revision of the project.Dataset revpath - A path to a dataset in a special format. They are supposed to specify paths to files, directories or data source revisions in a uniform way in the CLI.
dataset path - a path to a dataset in the following format:
<dataset path>:<format>
format is optional. If not specified, will try to detect automaticallyrevision path - a path to a data source revision in a project.
The syntax is:
<project path>@<revision>:<target name>, any part can be omitted.
-p/--project CLI arg.)
Local revpaths imply that the current project is used and this part
should be omitted.projectIf a path refers to project (i.e. target name is not set, or
this target is exactly specified), the target dataset is the result of
joining all the project data sources.
Otherwise, if the path refers to a data source revision, the
corresponding stage from the revision build tree will be used.
Dataset building concepts:
project, which represents
a joined dataset.
Each data source has a starting root node, which corresponds to the
original dataset. The internal graph nodes are stages.Other:
transform command.In Datumaro, most command-line commands operate on projects, but there are also few commands operating on datasets directly. There are 2 basic ways to use Datumaro from the command-line:
Use the convert, diff, merge
commands directly on existing datasets
Create a Datumaro project and operate on it:
createimporttransform and filtercommit, navigate over
them using checkout,
compare with diff, compute
statistics with statsexportproject infoBasically, a project is a combination of datasets, models and environment.
A project can contain an arbitrary number of datasets (data sources).
A project acts as a manager for them and allows to manipulate them
separately or as a whole, in which case it combines dataset items
from all the sources into one composite dataset. You can manage separate
datasets in a project by commands in the datum source
command line context.
Note that modifying operations (transform, filter, patch)
are applied in-place to the datasets by default.
If you want to interact with models, you need to add them to the project
first using the model add command.
A typical way to obtain Datumaro projects is to export tasks in CVAT UI.
Datumaro tries to combine a “Git for datasets” and a build system like
make or CMake for datasets in a single solution. Currently, Project
represents a Version Control System for datasets, which is based on Git and DVC
projects. Each project Revision describes a build tree of a dataset
with all the related metadata. A build tree consists of a number of data
sources and transformation stages. Each data source has its own set of build
steps (stages). Datumaro supposes copying of datasets and working in-place by
default. Modifying operations are recorded in the project, so any of the
dataset revisions can be reproduced when needed. Multiple dataset versions can
be stored in different branches with the common data shared.
Let’s consider an example of a build tree:
There are 2 data sources in the example project. The resulting dataset
is obtained by simple merging (joining) the results of the input datasets.
“Source 1” and “Source 2” are the names of data sources in the project. Each
source has several stages with their own names. The first stage (called “root”)
represents the original contents of a data source - the data at the
user-provided URL. The following stages represent operations, which needs to
be done with the data source to prepare the resulting dataset.
Roughly, such build tree can be created by the following commands (arguments are omitted for simplicity):
Now, the resulting dataset can be built with:
A project can contain an arbitrary number of Data Sources. Each Data Source
describes a dataset in a specific format. A project acts as a manager for
the data sources and allows to manipulate them separately or as a whole, in
which case it combines dataset items from all the sources into one composite
dataset. You can manage separate sources in a project by commands in
the datum source command
line context.
Datasets come in a wide variety of formats. Each dataset format defines its own data structure and rules on how to interpret the data. For example, the following data structure is used in COCO format:
/dataset/
- /images/<id>.jpg
- /annotations/
Datumaro supports complete datasets, having both image data and annotations, or incomplete ones, having annotations only. Incomplete datasets can be used to prepare images and annotations independently of each other, or to analyze or modify just the lightweight annotations without the need to download the whole dataset.
Check supported formats for more info about format specifications, supported import and export options and other details. The list of formats can be extended by custom plugins, check extending tips for information on this topic.
Let’s consider few examples describing what Datumaro does for you behind the scene.
The first example explains how working trees, working directories and the cache interact. Suppose, there is a dataset which we want to modify and export in some other format. To do it with Datumaro, we need to create a project and register the dataset as a data source:
The dataset will be copied to the working directory inside the project. It will be added to the project working tree.
After the dataset is added, we want to transform it and filter out some irrelevant samples, so we run the following commands:
The commands modify the data source inside the working directory, inplace. The operations done are recorded in the working tree.
Now, we want to make a new version of the dataset and make a snapshot in the
project cache. So we commit the working tree:
At this time, the data source is copied into the project cache and a new
project revision is created. The dataset operation history is saved, so
the dataset can be reproduced even if it is removed from the cache and the
working directory. Note, however, that the original dataset hash was not
computed, so Datumaro won’t be able to compare dataset hash on re-downloading.
If it is desired, consider making a commit with an unmodified data source.
After this, we do some other modifications to the dataset and make a new
commit. Note that the dataset is not cached, until a commit is done.
When the dataset is ready and all the required operations are done, we
can export it to the required format. We can export the resulting dataset,
or any previous stage.
Let’s extend the example. Imagine we have a project with 2 data sources. Roughly, it corresponds to the following set of commands:
Then, for some reasons, the project cache was cleaned from source1 revisions.
We also don’t have anything in the project working directories - suppose,
the user removed them to save disk space.
Let’s see what happens, if we call the diff command with 2 different
revisions now.
Datumaro needs to reproduce 2 dataset revisions requested so that they could be read and compared. Let’s see how the first dataset is reproduced step-by-step:
source1.stage2 will be looked for in the project cache. It won’t be
found, since the cache was cleaned.-p/--project command
parameter). In the example, the command is datum diff rev1:... rev2:...,
which means there is a project in the current directory, so the project
we are working with is not read-only. If a command target was specified as
datum diff <project>@<rev>:<source>, the project would be loaded
as read-only. If a project is read-only, we can’t do anything more to
reproduce the dataset and can only exit with an error (3a). The reason for
such behavior is that the dataset downloading can be quite expensive (in
terms of time, disk space etc.). It is supposed, that such side-effects
should be controlled manually.The source2 will be looked for the same way. In our case, it will be found
in the cache and returned. Once both datasets are restored and read, they
are compared.
Consider other situation. Let’s try to export the source1. Suppose
we have a clear project cache and the source1 has a copy in the working
directory.
Again, Datumaro needs to reproduce a dataset revision (stage) requested.
After the requested dataset is obtained, it is exported in the requested format.
To sum up, Datumaro tries to restore a dataset from the project cache or reproduce it from sources. It can be done as long as the source operations are recorded and any step data is available. Note that cache objects share common files, so if there are only annotation differences between datasets, or data sources contain the same images, there will only be a single copy of the related media files. This helps to keep storage use reasonable and avoid unnecessary data copies.
Example: create a project, add dataset, modify, restore an old version
List of supported formats:
image_info, instances, person_keypoints, captions, labels,panoptic, stuff)
labels are our extension - like instances with only category_idclassification, detection, segmentation (class, instances),
action_classification, person_layout)
bboxes)
bboxes, masks)
bboxes)
landmarks, bboxes)
classification, detection)
classification (python version))
classification)
classification)
segmentation)
segmentation)
segmentation, detection)
raw/tracklets/velodyne points)
pointcloud)
segmentation)
word_recognition, text_localization, text_segmentation)
person re-identification)
classification, person re-identification, landmarks)
classification, detection, landmarks)
classification, landmarks)
detection)
detection)
detection, pose estimation)
detection, pose estimation)
Datumaro does not separate datasets by tasks like classification, detection etc. Instead, datasets can have any annotations. When a dataset is exported in a specific format, only relevant annotations are exported.
It is possible to use classes that are not original to the format.
To do this, use dataset_meta.json.
{
"label_map": {"0": "background", "1": "car", "2": "person"},
"segmentation_colors": [[0, 0, 0], [255, 0, 0], [0, 0, 255]],
"background_label": "0"
}
label_map is a dictionary where the class ID is the key and
the class name is the value.segmentation_colors is a list of channel-wise values for each class.
This is only necessary for the segmentation task.background_label is a background label ID in the dataset.Datumaro supports the following media types:
To create an unlabelled dataset from an arbitrary directory with images use
image_dir and image_zip formats:
or, if you work with Datumaro API:
for using with a project:
for using as a dataset:
This will search for images in the directory recursively and add
them as dataset entries with names like <subdir1>/<subsubdir1>/<image_name1>.
The list of formats matches the list of supported image formats in OpenCV:
.jpg, .jpeg, .jpe, .jp2, .png, .bmp, .dib, .tif, .tiff, .tga, .webp, .pfm,
.sr, .ras, .exr, .hdr, .pic, .pbm, .pgm, .ppm, .pxm, .pnm
Once there is a Dataset instance, its items can be split into subsets,
renamed, filtered, joined with annotations, exported in various formats etc.
To use a video as an input, one should either create a plugin, which splits a video into frames, or split the video manually and import images.
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
--helpoutput of the specific command
Note: command parameters must be passed prior to the positional arguments.
Datumaro functionality is available with the datum command.
Usage:
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.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:
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:
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:
To use a proxy for downloading, configure it with the conventional curl environment variables.
Usage:
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:
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:
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
Example: create a new empty project in the current directory, remove the existing one
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:
--save-images option, which
allows to export dataset images along with annotations. The option is
disabled be default.--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:
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.
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+itemsWhen 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:
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:
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
Example: extract a dataset with images of the train subset
Example: extract a dataset with only large annotations of the cat class and
any non-persons
Example: extract a dataset with non-occluded annotations, remove empty images. Use data only from the “s1” source of the project.
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
patchcommand. 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:
<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:
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,
person, hand, head and foot
(? is used for optional parts)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
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:
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 usedThis command has multiple forms:
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:
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
This command outputs high level dataset information such as sample count, categories and subsets.
Usage:
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:
Print info about a project dataset:
datum info -p test_project/
Print info about a COCO-like dataset:
datum info path/to/dataset:coco
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)
This command computes various project statistics, such as:
Usage:
Parameters:
<target> (string) - Target
source revpath.
By default, computes statistics of the merged dataset.-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.Example:
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,
"dataset": {
"image mean": [
107.06903686941979,
79.12831698580979,
52.95829558185416
],
"image std": [
49.40237673503467,
43.29600731496902,
35.47373007603151
],
"images count": 100
},
"images count": 100,
"subsets": {},
"unannotated images": [
"img00051",
"img00052",
"img00053",
"img00054",
"img00055",
],
"unannotated images count": 5,
"unique images count": 97,
"repeating images count": 3,
"repeating images": [
[("img00057", "default"), ("img00058", "default")],
[("img00059", "default"), ("img00060", "default")],
[("img00061", "default"), ("img00062", "default")],
],
}
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
summaryUsage:
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 histogramExample : give warning when imbalance ratio of data with classification task over 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:
item_key is defined as,
bbox_template and mask_template are defined as,
numerical_stat_template is defined as,
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:
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:
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:
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:
frame_XXX to XXX:Basic dataset item manipulations:
rename - Renames dataset items by regular expressionid_from_image_name - Renames dataset items to their image filenamesreindex - Renames dataset items with numbersndr - Removes duplicated images from datasetsampler - Runs inference and leaves only the most representative imagesresize - Resizes images and annotations in the datasetSubset manipulations:
random_split - Splits dataset into subsets randomlysplit - Splits dataset into subsets for classification, detection,
segmentation or re-identificationmap_subsets - Renames and removes subsetsAnnotation manipulations:
remap_labels - Renames, adds or removes labels in datasetproject_labels - Sets dataset labels to the requested sequenceshapes_to_boxes - Replaces spatial annotations with bounding boxesboxes_to_masks - Converts bounding boxes to instance maskspolygons_to_masks - Converts polygons to instance masksmasks_to_polygons - Converts instance masks to polygonsanns_to_labels - Replaces annotations having labels with label annotationsmerge_instance_segments - Merges grouped spatial annotations into a maskcrop_covered_segments - Removes occluded segments of covered masksbbox_value_decrement - Subtracts 1 from bbox coordinatesExamples:
train and test subsets, ratio is 2:1person, cat, dog}, remove others, add missing.
Original labels (can be any): cat, dog, elephant, human
New labels: person (added), cat (kept), dog (kept)person to car and cat to dog,
keep bus, remove otherspattern with replacementframe_ from item idssampled and unsampled subsets, based on the model
confidence, which is stored in the scores annotation attribute.There are five methods of sampling (the -m/--method option):
topk - Return the k with high uncertainty datalowk - Return the k with low uncertainty datarandk - Return the random k datamixk - Return half to topk method and the rest to lowk methodrandtopk - First, select 3 times the number of k randomly, and return
the topk among them.-e parameter):
random - sample from removed data randomlysimilarity - sample from removed data with ascending-u parameter):
uniform - sample data with uniform distributioninverse - sample data with reciprocal of the numberdatum transform -t resize -- -dw 256 -dh 256
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 - 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
revisionUsage:
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
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:
Parameters:
-p, --project (string) - Directory of the project to operate on
(default: current directory).-h, --help - Print the help message and exit.Example output:
This command prints the history of the current project revision.
Prints lines in the following format:
<short commit hash> <commit message>
Usage:
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:
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:
Usage:
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 computationsExamples:
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
Note: this algorithm requires the model to return all (or a reasonable amount) the outputs and confidences unfiltered, i.e. all the
Labelannotations for classification models and all theBboxes for detection models. You can find examples of the expected model outputs intests/test_RISE.py
For OpenVINO models the output processing script would look like this:
Classification scenario:
Object Detection scenario:
Datumaro can execute deep learning models in various frameworks. Check the plugins section for more info.
Supported frameworks:
launchersModels need to be added to the Datumaro project first. It can be done with
the datum model add command.
Usage:
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.
Interpretation script for an OpenVINO detection model (convert.py):
You can find OpenVINO model interpreter samples in
datumaro/plugins/openvino/samples (instruction).
To remove a model from a project, use the datum model remove command.
Usage:
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:
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
These commands are specific for Data Sources. Read more about them here.
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:
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:
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
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:
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
Example: add an existing dataset into a project, avoid data copying
To add a dataset, we need to have it inside the project directory:
To remove a data source from a project, use the remove command.
Usage:
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:
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:
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:
datum project migrate -o <output/dir>Prints project configuration info such as available plugins, registered models, imported sources and build tree.
Usage:
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:
Print project info for the current working tree:
datum project info
Print project info for the previous revision:
datum project info HEAD~1
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
There are few ways to extend and customize Datumaro behavior, which is supported by plugins. Check our contribution guide for details on plugin implementation. In general, a plugin is a Python module. It must be put into a plugin directory:
<project_dir>/.datumaro/plugins for project-specific plugins<datumaro_dir>/plugins for global pluginsDatumaro provides several builtin plugins. Plugins can have dependencies, which need to be installed separately.
The plugin provides support of TensorFlow Detection API format, which includes boxes and masks.
Dependencies
The plugin depends on TensorFlow, which can be installed with pip:
This plugin allows to use Accuracy Checker to launch deep learning models from various frameworks (Caffe, MxNet, PyTorch, OpenVINO, …) through Accuracy Checker’s API.
Dependencies
The plugin depends on Accuracy Checker, which can be installed with pip:
To execute models with deep learning frameworks, they need to be installed too.
This plugin provides support for model inference with OpenVINO™.
Dependencies
The plugin depends on the OpenVINO™ Toolkit, which can be installed by following these instructions
Dataset reading is supported by Extractors and Importers. An Extractor produces a list of dataset items corresponding to the dataset. An Importer creates a project from the data source location. It is possible to add custom Extractors and Importers. To do this, you need to put an Extractor and Importer implementation scripts to a plugin directory.
Dataset writing is supported by Converters. A Converter produces a dataset of a specific format from dataset items. It is possible to add custom Converters. To do this, you need to put a Converter implementation script to a plugin directory.
A Transform is a function for altering a dataset and producing a new one. It can update dataset items, annotations, classes, and other properties. A list of available transforms for dataset conversions can be extended by adding a Transform implementation script into a plugin directory.
A list of available launchers for model execution can be extended by adding a Launcher implementation script into a plugin directory.
The OpenVINO™ telemetry library is used to collect basic information about Datumaro usage.
A short description of the information collected:
| Event | Description |
|---|---|
| version | Datumaro version |
| session start/end | Accessory event, there is no additional info here |
| {cli_command}_result | Datumaro command result with arguments passed* |
| error | Stack trace in case of exception* |
* All sensitive arguments, such as filesystem paths or names, are sanitized
To enable the collection of telemetry data, the ISIP consent file
must exist and contain 1, otherwise telemetry will be disabled.
The ISIP file can be created/modified by an OpenVINO installer
or manually and used by other OpenVINO™ tools.
The location of the ISIP consent file depends on the OS:
%localappdata%\Intel Corporation\isip,$HOME/intel/isip.The central part of the library is the Dataset class, which represents
a dataset and allows to iterate over its elements.
DatasetItem, an element of a dataset, represents a single
dataset entry with annotations - an image, video sequence, audio track etc.
It can contain only annotated data or meta information, only annotations, or
all of this.
Basic library usage and data flow:
Extractors and
merged into a DatasetConverterDatumaro has a number of dataset and annotation features:
train, val, test)The Dataset class from the datumaro.components.dataset module represents
a dataset, consisting of multiple DatasetItems. Annotations are
represented by members of the datumaro.components.extractor module,
such as Label, Mask or Polygon. A dataset can contain items from one or
multiple subsets (e.g. train, test, val etc.), the list of dataset
subsets is available in dataset.subsets().
A DatasetItem is an element of a dataset. Its id is the name of the
corresponding image, video frame, or other media being annotated.
An item can have some attributes, associated media info and annotations.
Datasets typically have annotations, and these annotations can
require additional information to be interpreted correctly. For instance, it
can be class names, class hierarchy, keypoint connections,
class colors for masks, class attributes.
Such information is stored in dataset.categories(), which is a mapping from
AnnotationType to a corresponding ...Categories class. Each annotation type
can have its Categories. Typically, there will be at least LabelCategories;
if there are instance masks, the dataset will contain MaskCategories etc.
The “main” type of categories is LabelCategories - annotations and other
categories use label indices from this object.
The main operation for a dataset is iteration over its elements
(DatasetItems). An item corresponds to a single image, a video sequence,
etc. There are also many other operations available, such as filtration
(dataset.select()), transformation (dataset.transform()),
exporting (dataset.export()) and others. A Dataset is an Iterable and
Extractor by itself.
A Dataset can be created from scratch by its class constructor.
Categories can be set immediately or later with the
define_categories() method, but only once. You can create a dataset filled
with initial DatasetItems with Dataset.from_iterable().
If you need to create a dataset from one or many other extractors
(or datasets), it can be done with Dataset.from_extractors().
If a dataset is created from multiple extractors with
Dataset.from_extractors(), the source datasets will be joined,
so their categories must match. If datasets have mismatching categories,
use the more complex IntersectMerge class from datumaro.components.operations,
which will merge all the labels and remap the shifted indices in annotations.
A Dataset can be loaded from an existing dataset on disk with
Dataset.import_from() (for arbitrary formats) and
Dataset.load() (for the Datumaro data format).
By default, Dataset works lazily, which means all the operations requiring
iteration over inputs will be deferred as much as possible. If you don’t want
such behavior, use the init_cache() method or wrap the code in
eager_mode (from datumaro.components.dataset), which will load all
the annotations into memory. The media won’t be loaded unless the data
is required, because it can quickly waste all the available memory.
You can check if the dataset is cached with the is_cache_initialized
attribute.
Once created, a dataset can be modified in batch mode with transforms or
directly with the put() and remove() methods. Dataset instances
record information about changes done, which can be obtained by get_patch().
The patch information is used automatically on saving and exporting to
reduce the amount of disk writes. Changes can be flushed with
flush_changes().
There are 2 methods of merging datasets in Datumaro:
This approach finds the corresponding DatasetItems in inputs,
finds equal annotations and leaves only the unique set of annotations.
This approach requires all the inputs to have categories with the same
labels (or no labels) in the same order.
This algorithm is applied automatically in Dataset.from_extractors()
and when the build targets are merged in the ProjectTree.make_dataset().
If datasets have mismatching categories, they can’t be merged by the simple approach, because it can lead to errors in the resulting annotations. For complex cases Datumaro provides a more sophisticated algorithm, which finds matching annotations by computing distances between them. Labels and attributes are deduced by voting, spatial annotations use the corresponding metrics like Intersection-over-Union (IoU), OKS, PDJ and others.
The categories of the input datasets are compared, the matching ones complement missing information in each other, the mismatching ones are appended after next. Label indices in annotations are shifted to the new values.
The complex algorithm is available in the IntersectMerge class
from datumaro.components.operations. It must be used explicitly.
This class also allows to check the inputs and the output dataset
for errors and problems.
Projects are intended for complex use of Datumaro. They provide means of
persistence, versioning, high-level operations for datasets and also
allow to extend Datumaro via plugins. A project provides
access to build trees and revisions, data sources, models, configuration,
plugins and cache. Projects can have multiple data sources, which are
joined on dataset creation. Project configuration is available
in project.config. To add a data source into a Project, use
the import_source() method. The build tree of the current working
directory can be converted to a Dataset with
project.working_tree.make_dataset().
The Environment class is responsible for accessing built-in and
project-specific plugins. For a Project object, there is an instance of
related Environment in project.env.
Check the Data Model section of the User Manual for more info about Project behavior and high-level details.
The framework provides functions to read and write datasets in specific formats.
It is supported by Extractors, Importers, and Converters.
Dataset reading is supported by Extractors and Importers:
Extractor produces a list of DatasetItems corresponding to the
dataset. Annotations are available in the DatasetItem.annotations list.
The SourceExtractor class is designed for loading simple, single-subset
datasets. It should be used by default. The Extractor base class should
be used when SourceExtractor’s functionality is not enough.Importer detects dataset files and generates dataset loading parameters
for the corresponding Extractors. Importers are optional, they
only extend the Extractor functionality and make them more flexible and
simple. They are mostly used to locate dataset subsets, but they also can
do some data compatibility checks and have other required logic.It is possible to add custom Extractors and Importers. To do this, you need
to put an Extractor and Importer implementations to a plugin directory.
Dataset writing is supported by Converters.
A Converter produces a dataset of a specific format from dataset items.
It is possible to add custom Converters. To do this, you need to put a
Converter implementation script to a plugin directory.
A Transform is a function for altering a dataset and producing a new one.
It can update dataset items, annotations, classes, and other properties.
A list of available transforms for dataset conversions can be extended by
adding a Transform implementation script into a plugin directory.
A list of available launchers for model execution can be extended by
adding a Launcher implementation script into a plugin directory.
Datumaro comes with a number of built-in formats and other tools, but it also can be extended by plugins. Plugins are optional components, which dependencies are not installed by default. In Datumaro there are several types of plugins, which include:
extractor - produces dataset items from data sourceimporter - recognizes dataset type and creates projectconverter - exports dataset to a specific formattransformation - modifies dataset items or other propertieslauncher - executes modelsA plugin is a regular Python module. It must be present in a plugin directory:
<project_dir>/.datumaro/plugins for project-specific plugins<datumaro_dir>/plugins for global pluginsA plugin can be used either via the Environment class instance,
or by regular module importing:
A plugin is a Python module with any name, which exports some symbols. Symbols,
starting with _ are not exported by default. To export a symbol,
inherit it from one of the special classes:
The exports list of the module can be used to override default behaviour:
There is also an additional class to modify plugin appearance in command line:
datumaro/plugins/
- my_plugin1/file1.py
- my_plugin1/file2.py
- my_plugin2.py
my_plugin1/file2.py contents:
my_plugin2.py contents:
Basically, the interface is divided on contexts and single commands. Contexts are semantically grouped commands, related to a single topic or target. Single commands are handy shorter alternatives for the most used commands and also special commands, which are hard to be put into any specific context. Docker is an example of similar approach.
Model-View-ViewModel (MVVM) UI pattern is used.
The original ADE20K 2017 dataset is available here.
The consistency set (for checking the annotation consistency) is available here.
Supported annotation types:
MasksSupported annotation attributes:
occluded (boolean): whether the object is occluded by another object<image_name>_atr.txtA Datumaro project with an ADE20k source can be created in the following way:
It is also possible to import the dataset using Python API:
ADE20K dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── subset1/
│ └── super_label_1/
│ ├── img1.jpg
│ ├── img1_atr.txt
│ ├── img1_parts_1.png
│ ├── img1_seg.png
│ ├── img2.jpg
│ ├── img2_atr.txt
│ └── ...
└── subset2/
├── img3.jpg
├── img3_atr.txt
├── img3_parts_1.png
├── img3_parts_2.png
├── img4.jpg
├── img4_atr.txt
├── img4_seg.png
└── ...
The mask images <image_name>_seg.png contain information about the object
class segmentation masks and also separate each class into instances.
The channels R and G encode the objects class masks.
The channel B encodes the instance object masks.
The mask images <image_name>_parts_N.png contain segmentation masks for parts
of objects, where N is a number indicating the level in the part hierarchy.
The annotation files <image_name>_atr.txt describe the content of each
image. Each line in the text file contains:
#. See example of dataset
here.To add custom classes, you can use dataset_meta.json.
Datumaro can convert an ADE20K dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports segmentation masks.
There are several ways to convert an ADE20k 2017 dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
The original ADE20K 2020 dataset is available here.
The consistency set (for checking the annotation consistency) is available here.
Supported annotation types:
MasksSupported annotation attributes:
occluded (boolean): whether the object is occluded by another object<image_name>.jsonA Datumaro project with an ADE20k source can be created in the following way:
It is also possible to import the dataset using Python API:
ADE20K dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── subset1/
│ ├── img1/ # directory with instance masks for img1
│ | ├── instance_001_img1.png
│ | ├── instance_002_img1.png
│ | └── ...
│ ├── img1.jpg
│ ├── img1.json
│ ├── img1_seg.png
│ ├── img1_parts_1.png
│ |
│ ├── img2/ # directory with instance masks for img2
│ | ├── instance_001_img2.png
│ | ├── instance_002_img2.png
│ | └── ...
│ ├── img2.jpg
│ ├── img2.json
│ └── ...
│
└── subset2/
├── super_label_1/
| ├── img3/ # directory with instance masks for img3
| | ├── instance_001_img3.png
| | ├── instance_002_img3.png
| | └── ...
| ├── img3.jpg
| ├── img3.json
| ├── img3_seg.png
| ├── img3_parts_1.png
| └── ...
|
├── img4/ # directory with instance masks for img4
| ├── instance_001_img4.png
| ├── instance_002_img4.png
| └── ...
├── img4.jpg
├── img4.json
├── img4_seg.png
└── ...
The mask images <image_name>_seg.png contain information about the object
class segmentation masks and also separate each class into instances.
The channels R and G encode the objects class masks.
The channel B encodes the instance object masks.
The mask images <image_name>_parts_N.png contain segmentation masks for
parts of objects, where N is a number indicating the level in the part
hierarchy.
The <image_name> directory contains instance masks for each
object in the image, these masks represent one-channel images,
each pixel of which indicates an affinity to a specific object.
The annotation files <image_name>.json describe the content of each image.
See our tests asset
for example of this file,
or check ADE20K toolkit for it.
To add custom classes, you can use dataset_meta.json.
Datumaro can convert an ADE20K dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports segmentation masks.
There are several ways to convert an ADE20k dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
The original CelebA dataset is available here.
Supported annotation types:
LabelPoints (landmarks)Supported attributes:
5_o_Clock_Shadow, Arched_Eyebrows, Attractive,
Bags_Under_Eyes, Bald, Bangs, Big_Lips, Big_Nose, Black_Hair,
Blond_Hair, Blurry, Brown_Hair, Bushy_Eyebrows, Chubby,
Double_Chin, Eyeglasses, Goatee, Gray_Hair, Heavy_Makeup,
High_Cheekbones, Male, Mouth_Slightly_Open, Mustache, Narrow_Eyes,
No_Beard, Oval_Face, Pale_Skin, Pointy_Nose, Receding_Hairline,
Rosy_Cheeks, Sideburns, Smiling, Straight_Hair, Wavy_Hair,
Wearing_Earrings, Wearing_Hat, Wearing_Lipstick, Wearing_Necklace,
Wearing_Necktie, Young (boolean)A Datumaro project with an align CelebA source can be created in the following way:
It is also possible to import the dataset using Python API:
Align CelebA dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── Anno/
│ ├── identity_CelebA.txt
│ ├── list_attr_celeba.txt
│ └── list_landmarks_align_celeba.txt
├── Eval/
│ └── list_eval_partition.txt
└── Img/
└── img_align_celeba/
├── 000001.jpg
├── 000002.jpg
└── ...
The identity_CelebA.txt file contains labels (required).
The list_attr_celeba.txt, list_landmarks_align_celeba.txt,
list_eval_partition.txt files contain attributes, bounding boxes,
landmarks and subsets respectively (optional).
The original CelebA dataset stores images in a .7z archive. The archive needs to be unpacked before importing.
To add custom classes, you can use dataset_meta.json.
Datumaro can convert an align CelebA dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports labels or landmarks.
There are several ways to convert an align CelebA dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
The original CelebA dataset is available here.
Supported annotation types:
LabelBboxPoints (landmarks)Supported attributes:
5_o_Clock_Shadow, Arched_Eyebrows, Attractive,
Bags_Under_Eyes, Bald, Bangs, Big_Lips, Big_Nose, Black_Hair,
Blond_Hair, Blurry, Brown_Hair, Bushy_Eyebrows, Chubby,
Double_Chin, Eyeglasses, Goatee, Gray_Hair, Heavy_Makeup,
High_Cheekbones, Male, Mouth_Slightly_Open, Mustache, Narrow_Eyes,
No_Beard, Oval_Face, Pale_Skin, Pointy_Nose, Receding_Hairline,
Rosy_Cheeks, Sideburns, Smiling, Straight_Hair, Wavy_Hair,
Wearing_Earrings, Wearing_Hat, Wearing_Lipstick, Wearing_Necklace,
Wearing_Necktie, Young (boolean)A Datumaro project with a CelebA source can be created in the following way:
It is also possible to import the dataset using Python API:
CelebA dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── Anno/
│ ├── identity_CelebA.txt
│ ├── list_attr_celeba.txt
│ ├── list_bbox_celeba.txt
│ └── list_landmarks_celeba.txt
├── Eval/
│ └── list_eval_partition.txt
└── Img/
└── img_celeba/
├── 000001.jpg
├── 000002.jpg
└── ...
The identity_CelebA.txt file contains labels (required).
The list_attr_celeba.txt, list_bbox_celeba.txt,
list_landmarks_celeba.txt, list_eval_partition.txt files contain
attributes, bounding boxes, landmarks and subsets respectively
(optional).
The original CelebA dataset stores images in a .7z archive. The archive needs to be unpacked before importing.
To add custom classes, you can use dataset_meta.json.
Datumaro can convert a CelebA dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports labels, bounding boxes or landmarks.
There are several ways to convert a CelebA dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
CIFAR format specification is available here.
Supported annotation types:
LabelDatumaro supports Python version CIFAR-10/100.
The difference between CIFAR-10 and CIFAR-100 is how labels are stored
in the meta files (batches.meta or meta) and in the annotation files.
The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image
comes with a “fine” label (the class to which it belongs) and a “coarse” label
(the superclass to which it belongs). In CIFAR-10 there are no superclasses.
CIFAR formats contain 32 x 32 images. As an extension, Datumaro supports reading and writing of arbitrary-sized images.
The CIFAR dataset is available for free download:
A Datumaro project with a CIFAR source can be created in the following way:
It is possible to specify project name and project directory. Run
datum create --help for more information.
CIFAR-10 dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── batches.meta
├── <subset_name1>
├── <subset_name2>
└── ...
CIFAR-100 dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── meta
├── <subset_name1>
├── <subset_name2>
└── ...
Dataset files use the Pickle data format.
Meta files:
CIFAR-10:
num_cases_per_batch: 1000
label_names: list of strings (['airplane', 'automobile', 'bird', ...])
num_vis: 3072
CIFAR-100:
fine_label_names: list of strings (['apple', 'aquarium_fish', ...])
coarse_label_names: list of strings (['aquatic_mammals', 'fish', ...])
Annotation files:
Common:
'batch_label': 'training batch 1 of <N>'
'data': numpy.ndarray of uint8, layout N x C x H x W
'filenames': list of strings
If images have non-default size (32x32) (Datumaro extension):
'image_sizes': list of (H, W) tuples
CIFAR-10:
'labels': list of strings
CIFAR-100:
'fine_labels': list of integers
'coarse_labels': list of integers
To add custom classes, you can use dataset_meta.json.
Datumaro can convert a CIFAR dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports the classification task (e.g. MNIST, ImageNet, PascalVOC, etc.)
There are several ways to convert a CIFAR dataset to other dataset formats using CLI:
Or, using Python API:
There are several ways to convert a dataset to CIFAR format:
Extra options for exporting to CIFAR format:
--save-images allow to export dataset with saving images
(by default False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting the dataset (by default .png)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)The format (CIFAR-10 or CIFAR-100) in which the dataset will be
exported depends on the presence of superclasses in the LabelCategories.
Datumaro supports filtering, transformation, merging etc. for all formats and for the CIFAR format in particular. Follow the user manual to get more information about these operations.
There are several examples of using Datumaro operations to solve particular problems with CIFAR dataset:
Convert a CIFAR dataset to ImageNet format, keep only images with the
dog class present:
Examples of using this format from the code can be found in the format tests
The original MPII Human Pose Dataset is available here.
Supported annotation types:
BboxPointsSupported attributes:
center (a list with two coordinates of the center point
of the object)scale (float)A Datumaro project with an MPII Human Pose Dataset source can be created in the following way:
It is also possible to import the dataset using Python API:
MPII Human Pose Dataset directory should have the following structure:
dataset/
├── mpii_human_pose_v1_u12_1.mat
├── 000000001.jpg
├── 000000002.jpg
├── 000000003.jpg
└── ...
Datumaro can convert an MPII Human Pose Dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports bounding boxes or points.
There are several ways to convert an MPII Human Pose Dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
The original MPII Human Pose Dataset is available here.
Supported annotation types:
BboxPointsSupported attributes:
center (a list with two coordinates of the center point
of the object)scale (float)A Datumaro project with an MPII Human Pose Dataset (JSON) source can be created in the following way:
It is also possible to import the dataset using Python API:
MPII Human Pose Dataset (JSON) directory should have the following structure:
dataset/
├── jnt_visible.npy # optional
├── mpii_annotations.json
├── mpii_headboxes.npy # optional
├── mpii_pos_gt.npy # optional
├── 000000001.jpg
├── 000000002.jpg
├── 000000003.jpg
└── ...
Datumaro can convert an MPII Human Pose Dataset (JSON) into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports bounding boxes or points.
There are several ways to convert an MPII Human Pose Dataset (JSON) to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
The original SYNTHIA dataset is available here.
Datumaro supports all SYNTHIA formats except SYNTHIA-AL.
Supported annotation types:
MaskSupported annotation attributes:
dynamic_object (boolean): whether the object movingA Datumaro project with a SYNTHIA source can be created in the following way:
It is also possible to import the dataset using Python API:
SYNTHIA dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── GT/
│ ├── COLOR/
│ │ ├── Stereo_Left/
│ │ │ ├── Omni_B
│ │ │ │ ├── 000000.png
│ │ │ │ ├── 000001.png
│ │ │ │ └── ...
│ │ │ └── ...
│ │ └── Stereo_Right
│ │ ├── Omni_B
│ │ │ ├── 000000.png
│ │ │ ├── 000001.png
│ │ │ └── ...
│ │ └── ...
│ └── LABELS
│ ├── Stereo_Left
│ │ ├── Omni_B
│ │ │ ├── 000000.png
│ │ │ ├── 000001.png
│ │ │ └── ...
│ │ └── ...
│ └── Stereo_Right
│ ├── Omni_B
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
│ └── ...
└── RGB
├── Stereo_Left
│ ├── Omni_B
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
│ └── ...
└── Stereo_Right
├── Omni_B
│ ├── 000000.png
│ ├── 000001.png
│ └── ...
└── ...
RGB folder containing standard RGB images used for training.GT/LABELS folder containing containing PNG files (one per image).
Annotations are given in three channels. The red channel contains
the class of that pixel. The green channel contains the class only
for those objects that are dynamic (cars, pedestrians, etc.),
otherwise it contains 0.GT/COLOR folder containing png files (one per image).
Annotations are given using a color representation.When importing a dataset, only GT/LABELS folder will be used.
If it is missing, GT/COLOR folder will be used.
The original dataset also contains depth information, but Datumaro does not currently support it.
To add custom classes, you can use dataset_meta.json.
Datumaro can convert a SYNTHIA dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports segmentation masks.
There are several ways to convert a SYNTHIA dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in the format tests
VoTT (Visual Object Tagging Tool) is an open source annotation tool released by Microsoft. VoTT CSV is the format used by VoTT when the user exports a project and selects “CSV” as the export format.
Supported annotation types:
BboxA Datumaro project with a VoTT CSV source can be created in the following way:
It is also possible to import the dataset using Python API:
VoTT CSV dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── img0001.jpg
├── img0002.jpg
├── img0003.jpg
├── img0004.jpg
├── ...
├── test-export.csv
├── train-export.csv
└── ...
To add custom classes, you can use dataset_meta.json.
Datumaro can convert a VoTT CSV dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports bounding boxes.
There are several ways to convert a VoTT CSV dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in VoTT CSV tests.
VoTT (Visual Object Tagging Tool) is an open source annotation tool released by Microsoft. VoTT JSON is the format used by VoTT when the user exports a project and selects “VoTT JSON” as the export format.
Supported annotation types:
BboxA Datumaro project with a VoTT JSON source can be created in the following way:
It is also possible to import the dataset using Python API:
VoTT JSON dataset directory should have the following structure:
dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── img0001.jpg
├── img0002.jpg
├── img0003.jpg
├── img0004.jpg
├── ...
├── test-export.json
├── train-export.json
└── ...
To add custom classes, you can use dataset_meta.json.
Datumaro can convert a VoTT JSON dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports bounding boxes.
There are several ways to convert a VoTT JSON dataset to other dataset formats using CLI:
Or, using Python API:
Examples of using this format from the code can be found in VoTT JSON tests.
Cityscapes format overview is available here.
Cityscapes format specification is available here.
Supported annotation types:
MasksSupported annotation attributes:
is_crowd (boolean). Specifies if the annotation label can
distinguish between different instances.
If False, the annotation id field encodes the instance id.The Cityscapes dataset is available for free download.
A Datumaro project with a Cityscapes source can be created in the following way:
Cityscapes dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-Cityscapes labels (optional)
├── label_colors.txt # a list of non-Cityscapes labels in other format (optional)
├── imgsFine/
│ ├── leftImg8bit
│ │ ├── <split: train,val, ...>
│ │ | ├── {city1}
│ │ │ | ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_leftImg8bit.png
│ │ │ │ └── ...
│ │ | ├── {city2}
│ │ │ └── ...
│ │ └── ...
└── gtFine/
├── <split: train,val, ...>
│ ├── {city1}
│ | ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_color.png
│ | ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_instanceIds.png
│ | ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_labelIds.png
│ │ └── ...
│ ├── {city2}
│ └── ...
└── ...
Annotated files description:
*_leftImg8bit.png - left images in 8-bit LDR format*_color.png - class labels encoded by its color*_labelIds.png - class labels are encoded by its index*_instanceIds.png - class and instance labels encoded by an instance ID.
The pixel values encode class and the individual instance: the integer part
of a division by 1000 of each ID provides class ID, the remainder
is the instance ID. If a certain annotation describes multiple instances,
then the pixels have the regular ID of that classTo add custom classes, you can use dataset_meta.json
and label_colors.txt.
If the dataset_meta.json is not represented in the dataset, then
label_colors.txt will be imported if possible.
In label_colors.txt you can define custom color map and non-cityscapes labels,
for example:
# label_colors [color_rgb name]
0 124 134 elephant
To make sure that the selected dataset has been added to the project, you can
run datum project info, which will display the project information.
Datumaro can convert a Cityscapes dataset into any other format Datumaro supports. To get the expected result, convert the dataset to formats that support the segmentation task (e.g. PascalVOC, CamVID, etc.)
There are several ways to convert a Cityscapes dataset to other dataset formats using CLI:
Or, using Python API:
There are several ways to convert a dataset to Cityscapes format:
Extra options for exporting to Cityscapes format:
--save-images allow to export dataset with saving images
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original or use .png, if none)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)--label_map allow to define a custom colormap. Example:Datumaro supports filtering, transformation, merging etc. for all formats and for the Cityscapes format in particular. Follow the user manual to get more information about these operations.
There are several examples of using Datumaro operations to solve particular problems with a Cityscapes dataset:
Examples of using this format from the code can be found in the format tests
COCO format specification is available here.
The dataset has annotations for multiple tasks. Each task has its own format
in Datumaro, and there is also a combined coco format, which includes all
the available tasks. The sub-formats have the same options as the “main”
format and only limit the set of annotation files they work with. To work with
multiple formats, use the corresponding option of the coco format.
Supported tasks / formats:
cocococo_captioncoco_instancescoco_panopticcoco_person_keypointscoco_stuffcoco_image_infococo_labels. The format is like
Object Detection, but uses only category_id and score annotation fields.Supported annotation types (depending on the task):
Caption (captions)Label (label, Datumaro extension)Bbox (instances, person keypoints)Polygon (instances, person keypoints)Mask (instances, person keypoints, panoptic, stuff)Points (person keypoints)Supported annotation attributes:
is_crowd (boolean; on bbox, polygon and mask annotations) - Indicates
that the annotation covers multiple instances of the same class.score (number; range [0; 1]) - Indicates the confidence in this
annotation. Ground truth annotations always have 1.attributes section of the annotation descriptor.The COCO dataset is available for free download:
Images:
Annotations:
A Datumaro project with a COCO source can be created in the following way:
It is possible to specify project name and project directory. Run
datum create --help for more information.
Extra options for adding a source in the COCO format:
--keep-original-category-ids: Add dummy label categories so that
category indexes in the imported data source correspond to the category IDs
in the original annotation file.A COCO dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── images/
│ ├── train/
│ │ ├── <image_name1.ext>
│ │ ├── <image_name2.ext>
│ │ └── ...
│ └── val/
│ ├── <image_name1.ext>
│ ├── <image_name2.ext>
│ └── ...
└── annotations/
├── <task>_<subset_name>.json
└── ...
For the panoptic task, a dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── images/
│ ├── train/
│ │ ├── <image_name1.ext>
│ │ ├── <image_name2.ext>
│ │ └── ...
│ ├── val/
│ │ ├── <image_name1.ext>
│ │ ├── <image_name2.ext>
│ │ └── ...
└── annotations/
├── panoptic_train/
│ ├── <image_name1.ext>
│ ├── <image_name2.ext>
│ └── ...
├── panoptic_train.json
├── panoptic_val/
│ ├── <image_name1.ext>
│ ├── <image_name2.ext>
│ └── ...
└── panoptic_val.json
Annotation files must have the names like <task_name>_<subset_name>.json.
The year is treated as a part of the subset name.
If the annotation file name does’t match this pattern, use one of the
task-specific formats instead of plain coco: coco_captions,
coco_image_info, coco_instances, coco_labels, coco_panoptic,
coco_person_keypoints, coco_stuff. In this case all items of the
dataset will be added to the default subset.
To add custom classes, you can use dataset_meta.json.
You can import a dataset for one or several tasks instead of the whole dataset. This option also allows to import annotation files with non-default names. For example:
To make sure that the selected dataset has been added to the project, you can
run datum project info, which will display the project information.
Notes:
Datumaro can convert COCO dataset into any other format Datumaro supports. To get the expected result, convert the dataset to formats that support the specified task (e.g. for panoptic segmentation - VOC, CamVID)
There are several ways to convert a COCO dataset to other dataset formats using CLI:
Or, using Python API:
There are several ways to convert a dataset to COCO format:
Extra options for exporting to COCO format:
--save-images allow to export dataset with saving images
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original or use .jpg, if none)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)--segmentation-mode MODE allow to specify save mode for instance
segmentation:
guess)--crop-covered allow to crop covered segments so that background objects
segmentation was more accurate (by default False)--allow-attributes ALLOW_ATTRIBUTES allow export of attributes
(by default True). The parameter enables or disables writing
the custom annotation attributes to the “attributes” annotation
field. This field is an extension to the original COCO format--reindex REINDEX allow to assign new indices to images and annotations,
useful to avoid merge conflicts (by default False).
This option allows to control if the images and
annotations must be given new indices. It can be useful, when
you want to preserve the original indices in the produced dataset.
Consider having this option enabled when converting from other formats
or merging datasets to avoid conflicts--merge-images allow to save all images into a single directory
(by default False). The parameter controls the output directory for
images. When enabled, the dataset images are saved into a single
directory, otherwise they are saved in separate directories by subsets.--tasks TASKS allow to specify tasks for export dataset,
by default Datumaro uses all tasks. Example:Datumaro supports filtering, transformation, merging etc. for all formats and for the COCO format in particular. Follow the user manual to get more information about these operations.
There are several examples of using Datumaro operations to solve particular problems with a COCO dataset:
Examples of using this format from the code can be found in the format tests
The image zip format allows to export/import unannotated datasets with images to/from a zip archive. The format doesn’t support any annotations or attributes.
There are several ways to import unannotated datasets to your Datumaro project:
The directory with zip archives must have the following structure:
└── foo/
├── archive1.zip/
| ├── image_1.jpg
| ├── image_2.png
| ├── subdir/
| | ├── image_3.jpg
| | └── ...
| └── ...
├── archive2.zip/
| ├── image_101.jpg
| ├── image_102.jpg
| └── ...
...
Images in the archives must have a supported extension, follow the user manual to see the supported extensions.
Datumaro can convert image zip dataset into any other format Datumaro supports. For example:
Or, using Python API:
Example: exporting images from a VOC dataset to zip archives:
Extra options for exporting to image_zip format:
--save-images allow to export dataset with saving images
(default: False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting dataset (default: use original or .jpg, if none)--name name of output zipfile (default: default.zip)--compression allow to specify archive compression method.
Available methods:
ZIP_STORED, ZIP_DEFLATED, ZIP_BZIP2, ZIP_LZMA (default: ZIP_STORED).
Follow zip documentation
for more information.Examples of using this format from the code can be found in the format tests
Velodyne Points / KITTI Raw 3D data format homepage is available here.
Velodyne Points / KITTI Raw 3D data format specification is available here.
Supported annotation types:
Cuboid3d (represent tracks)Supported annotation attributes:
truncation (write, string), possible values: truncation_unset,
in_image, truncated, out_image, behind_image (case-independent).occlusion (write, string), possible values: occlusion_unset, visible,
partly, fully (case-independent). This attribute has priority
over occluded.occluded (read/write, boolean)keyframe (read/write, boolean). Responsible for occlusion_kf field.track_id (read/write, integer). Indicates the group over frames for
annotations, represent tracks.Supported image attributes:
frame (read/write, integer). Indicates frame number of the image.The velodyne points/KITTI Raw dataset is available for download here and here.
KITTI Raw dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── image_00/ # optional, aligned images from different cameras
│ └── data/
│ ├── <name1.ext>
│ └── <name2.ext>
├── image_01/
│ └── data/
│ ├── <name1.ext>
│ └── <name2.ext>
...
│
├── velodyne_points/ # optional, 3d point clouds
│ └── data/
│ ├── <name1.pcd>
│ └── <name2.pcd>
├── tracklet_labels.xml
└── frame_list.txt # optional, required for custom image names
The format does not support arbitrary image names and paths, but Datumaro provides an option to use a special index file to allow this.
frame_list.txt contents:
12345 relative/path/to/name1/from/data
46 relative/path/to/name2/from/data
...
To add custom classes, you can use dataset_meta.json.
A Datumaro project with a KITTI source can be created in the following way:
To make sure that the selected dataset has been added to the project,
you can run datum project info, which will display the project and dataset
information.
Datumaro can convert a KITTI Raw dataset into any other format Datumaro supports.
Such conversion will only be successful if the output format can represent the type of dataset you want to convert, e.g. 3D point clouds can be saved in Supervisely Point Clouds format, but not in COCO keypoints.
There are several ways to convert a KITTI Raw dataset to other dataset formats:
Or, using Python API:
There are several ways to convert a dataset to KITTI Raw format:
Extra options for exporting to KITTI Raw format:
--save-images allow to export dataset with saving images. This will
include point clouds and related images (by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original or use .png, if none)--reindex assigns new indices to frames and tracks. Allows annotations
without track_id attribute (they will be exported as single-frame tracks).--allow-attrs allows writing arbitrary annotation attributes. They will
be written in <annotations> section of <poses><item>
(disabled by default)Examples of using this format from the code can be found in the format tests
The KITTI dataset has many annotations for different tasks. Datumaro supports only a few of them.
Supported tasks / formats:
kitti_detection
The format specification is available in README.md here.kitti_segmentation
The format specification is available in README.md here.Supported annotation types:
Bbox (object detection)Mask (segmentation)Supported annotation attributes:
truncated (boolean) - indicates that the bounding box specified for
the object does not correspond to the full extent of the objectoccluded (boolean) - indicates that a significant portion of the object
within the bounding box is occluded by another objectscore (float) - indicates confidence in detectionThe KITTI left color images for object detection are available here. The KITTI object detection labels are available here. The KITTI segmentation dataset is available here.
A Datumaro project with a KITTI source can be created in the following way:
It is possible to specify project name and project directory. Run
datum create --help for more information.
KITTI detection dataset directory should have the following structure:
└─ Dataset/
├── testing/
│ └── image_2/
│ ├── <name_1>.<img_ext>
│ ├── <name_2>.<img_ext>
│ └── ...
└── training/
├── image_2/ # left color camera images
│ ├── <name_1>.<img_ext>
│ ├── <name_2>.<img_ext>
│ └── ...
└─── label_2/ # left color camera label files
├── <name_1>.txt
├── <name_2>.txt
└── ...
KITTI segmentation dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── label_colors.txt # optional, color map for non-original segmentation labels
├── testing/
│ └── image_2/
│ ├── <name_1>.<img_ext>
│ ├── <name_2>.<img_ext>
│ └── ...
└── training/
├── image_2/ # left color camera images
│ ├── <name_1>.<img_ext>
│ ├── <name_2>.<img_ext>
│ └── ...
├── label_2/ # left color camera label files
│ ├── <name_1>.txt
│ ├── <name_2>.txt
│ └── ...
├── instance/ # instance segmentation masks
│ ├── <name_1>.png
│ ├── <name_2>.png
│ └── ...
├── semantic/ # semantic segmentation masks (labels are encoded by its id)
│ ├── <name_1>.png
│ ├── <name_2>.png
│ └── ...
└── semantic_rgb/ # semantic segmentation masks (labels are encoded by its color)
├── <name_1>.png
├── <name_2>.png
└── ...
To add custom classes, you can use dataset_meta.json
and label_colors.txt.
If the dataset_meta.json is not represented in the dataset, then
label_colors.txt will be imported if possible.
You can import a dataset for specific tasks of KITTI dataset instead of the whole dataset, for example:
To make sure that the selected dataset has been added to the project, you can
run datum project info, which will display the project information.
Datumaro can convert a KITTI dataset into any other format Datumaro supports.
Such conversion will only be successful if the output
format can represent the type of dataset you want to convert,
e.g. segmentation annotations can be
saved in Cityscapes format, but not as COCO keypoints.
There are several ways to convert a KITTI dataset to other dataset formats:
Or, using Python API:
There are several ways to convert a dataset to KITTI format:
Extra options for exporting to KITTI format:
--save-images allow to export dataset with saving images
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original or use .png, if none)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)--apply-colormap APPLY_COLORMAP allow to use colormap for class masks
(in folder semantic_rgb, by default True)--label_map allow to define a custom colormap. Example:--tasks TASKS allow to specify tasks for export dataset,
by default Datumaro uses all tasks. Example:--allow-attributes ALLOW_ATTRIBUTES allow export of attributes
(by default True).Datumaro supports filtering, transformation, merging etc. for all formats and for the KITTI format in particular. Follow the user manual to get more information about these operations.
There are several examples of using Datumaro operations to solve particular problems with KITTI dataset:
Examples of using this format from the code can be found in the format tests
LFW (Labeled Faces in the Wild Home) it’s dataset for face identification task, specification for this format is available here. You can also download original LFW dataset here.
Original dataset contains images with people faces. For each image contains information about person’s name, as well as information about images that matched with this person and mismatched with this person. Also LFW contains additional information about landmark points on the face.
Supported annotation types:
LabelPoints (face landmark points)Supported attributes:
negative_pairs: list with names of mismatched persons;positive_pairs: list with names of matched persons;Importing LFW dataset into the Datumaro project:
datum create
datum import -f lfw <path_to_lfw_dataset>
See more information about adding datasets to the project in the docs.
Also you can import LFW dataset from Python API:
For successful importing the LFW dataset, the directory with it should has the following structure:
<path_to_lfw_dataset>/
├── subset_1
│ ├── annotations
│ │ ├── landmarks.txt # list with landmark points for each image
│ │ ├── pairs.txt # list of matched and mismatched pairs of person
│ │ └── people.txt # optional file with a list of persons name
│ └── images
│ ├── name0
│ │ ├── name0_0001.jpg
│ │ ├── name0_0002.jpg
│ │ ├── ...
│ ├── name1
│ │ ├── name1_0001.jpg
│ │ ├── name1_0002.jpg
│ │ ├── ...
├── subset_2
│ ├── ...
├── ...
Full description of annotation *.txt files available
here.
With Datumaro you can convert LFW dataset into any other
format Datumaro supports.
Pay attention that this format should also support Label and/or Points
annotation types.
There is few ways to convert LFW dataset into other format:
# Converting to ImageNet with `convert` command:
datum convert -if lfw -i ./lfw_dataset \
-f imagenet -o ./output_dir -- --save-images
# Converting to VggFace2 through the Datumaro project:
datum create
datum add -f lfw ./lfw_dataset
datum export -f vgg_face2 -o ./output_dir2
Note: some formats have extra export options. For particular format see the docs to get information about it.
With Datumaro you can export dataset that has Label or/and Points
annotations to the LFW format, example:
# Converting VGG Face2 dataset into the LFW format
datum convert -if vgg_face2 -i ./voc_dataset \
-f lfw -o ./output_dir
# Export dataaset to the LFW format through the Datumaro project:
datum create
datum import -f voc_classification ../vgg_dataset
datum export -f lfw -o ./output_dir -- --save-images --image-ext png
Available extra export options for LFW dataset format:
--save-images allow to export dataset with saving images.
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original)Mapillary Vistas dataset homepage is available here. After registration the dataset will be available for downloading. The specification for this format contains in the root directory of original dataset.
Supported annotation types:
- Mask (class, instances, panoptic)
- Polygon
Supported atttibutes:
- is_crowd(boolean; on panoptic mask): Indicates that the annotation
covers multiple instances of the same class.
Use these instructions to import Mapillary Vistas dataset into Datumaro project:
Note: the directory with dataset should be subdirectory of the project directory.
Note: there is no opportunity to import both instance and panoptic masks for one dataset.
Use one of subformats (mapillary_vistas_instances, mapillary_vistas_panoptic),
if your dataset contains both panoptic and instance masks:
Extra options for adding a source in the Mapillary Vistas format:
--use-original-config: Use original config_*.json file for your version of
Mapillary Vistas dataset. This options can helps to import dataset, in case
when you don’t have config_*.json file, but your dataset is using original
categories of Mapillary Vistas dataset. The version of dataset will be detect
by the name of annotation directory in your dataset (v1.2 or v2.0).--keep-original-category-ids: Add dummy label categories so that
category indexes in the imported data source correspond to the category IDs
in the original annotation file.Example of using extra options:
Mapillary Vistas dataset has two versions: v1.2, v2.0. They differ in the number of classes, the name of the classes, supported types of annotations, and the names of the directory with annotations. So, the directory with dataset should have one of these structures:
To add custom classes, you can use dataset_meta.json.
See examples of annotation files in test assets.
MNIST format specification is available here.
Fashion MNIST format specification is available here.
MNIST in CSV format specification is available here.
The dataset has several data formats available. Datumaro supports the binary (Python pickle) format and the CSV variant. Each data format is covered by a separate Datumaro format.
Supported formats:
mnistmnist_csvSupported annotation types:
LabelThe format only supports single channel 28 x 28 images.
The MNIST dataset is available for free download:
The Fashion MNIST dataset is available for free download:
The MNIST in CSV dataset is available for free download:
A Datumaro project with a MNIST source can be created in the following way:
MNIST dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── labels.txt # a list of non-digit labels in other format (optional)
├── t10k-images-idx3-ubyte.gz
├── t10k-labels-idx1-ubyte.gz
├── train-images-idx3-ubyte.gz
└── train-labels-idx1-ubyte.gz
MNIST in CSV dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── labels.txt # a list of non-digit labels in other format (optional)
├── mnist_test.csv
└── mnist_train.csv
To add custom classes, you can use dataset_meta.json
and labels.txt.
If the dataset_meta.json is not represented in the dataset, then
labels.txt will be imported if possible.
For example, labels.txt for Fashion MNIST the following contents:
T-shirt/top
Trouser
Pullover
Dress
Coat
Sandal
Shirt
Sneaker
Bag
Ankle boot
Datumaro can convert a MNIST dataset into any other format Datumaro supports. To get the expected result, convert the dataset to formats that support the classification task (e.g. CIFAR-10/100, ImageNet, PascalVOC, etc.)
There are several ways to convert a MNIST dataset to other dataset formats:
Or, using Python API:
These steps also will work for MNIST in CSV, if you use mnist_csv
instead of mnist.
There are several ways to convert a dataset to MNIST format:
Extra options for exporting to MNIST format:
--save-images allow to export dataset with saving images
(by default False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting dataset (by default .png)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)These commands also work for MNIST in CSV if you use mnist_csv instead of mnist.
Datumaro supports filtering, transformation, merging etc. for all formats and for the MNIST format in particular. Follow the user manual to get more information about these operations.
There are several examples of using Datumaro operations to solve particular problems with MNIST dataset:
Convert MNIST dataset to ImageNet format, keep only images with 3 class
presented:
Examples of using this format from the code can be found in the binary format tests and csv format tests
ICDAR is a dataset for text recognition task, it’s available for download here. There is exists two most popular version of this dataset: ICDAR13 and ICDAR15, Datumaro supports both of them.
Original dataset contains the following subformats:
Supported types of annotations:
CaptionPolygon, BboxMaskSupported attributes:
text: transcription of text is inside a Polygon/Bbox.index: identifier of the annotation object, which is encoded in the mask
and coincides with the line number in which the description
of this object is written;text: transcription of text is inside a Mask;color: RGB values of the color corresponding text in the mask image
(three numbers separated by space);center: coordinates of the center of text
(two numbers separated by space).There is few ways to import ICDAR dataset with Datumaro:
datum create
datum import -f icdar_text_localization <text_localization_dataset>
datum import -f icdar_text_segmentation <text_segmentation_dataset>
datum import -f icdar_word_recognition <word_recognition_dataset>
Dataset with ICDAR dataset should have the following structure:
For icdar_word_recognition
<dataset_path>/
├── <subset_name_1>
│ ├── gt.txt
│ └── images
│ ├── word_1.png
│ ├── word_2.png
│ ├── ...
├── <subset_name_2>
├── ...
For icdar_text_localization
<dataset_path>/
├── <subset_name_1>
│ ├── gt_img_1.txt
│ ├── gt_img_2.txt
│ ├── ...
│ └── images
│ ├── img_1.png
│ ├── img_2.png
│ ├── ...
├── <subset_name_2>
│ ├── ...
├── ...
For icdar_text_segmentation
<dataset_path>/
├── <subset_name_1>
│ ├── image_1_GT.bmp # mask for image_1
│ ├── image_1_GT.txt # description of mask objects on the image_1
│ ├── image_2_GT.bmp
│ ├── image_2_GT.txt
│ ├── ...
│ └── images
│ ├── image_1.png
│ ├── image_2.png
│ ├── ...
├── <subset_name_2>
│ ├── ...
├── ...
See more information about adding datasets to the project in the docs.
Datumaro can convert ICDAR dataset into any other format Datumaro supports. Examples:
# converting ICDAR text segmentation dataset into the VOC with `convert` command
datum convert -if icdar_text_segmentation -i source_dataset \
-f voc -o export_dir -- --save-images
# converting ICDAR text localization into the LabelMe through Datumaro project
datum create
datum import -f icdar_text_localization source_dataset
datum export -f label_me -o ./export_dir -- --save-images
Note: some formats have extra export options. For particular format see the docs to get information about it.
With Datumaro you can also convert your dataset to one of the ICDAR formats, but to get expected result, the source dataset should contain required attributes, described in previous section.
Note: in case with
icdar_text_segmentationformat, if your dataset contains masks without attributecolorthen it will be generated automatically.
Available extra export options for ICDAR dataset formats:
--save-images allow to export dataset with saving images.
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original)A description of the Open Images Dataset (OID) format is available here. Datumaro supports versions 4, 5 and 6.
Supported annotation types:
Label (human-verified image-level labels)Bbox (bounding boxes)Mask (segmentation masks)Supported annotation attributes:
Labels
score (read/write, float).
The confidence level from 0 to 1.
A score of 0 indicates that
the image does not contain objects of the corresponding class.Bounding boxes
score (read/write, float).
The confidence level from 0 to 1.
In the original dataset this is always equal to 1,
but custom datasets may be created with arbitrary values.occluded (read/write, boolean).
Whether the object is occluded by another object.truncated (read/write, boolean).
Whether the object extends beyond the boundary of the image.is_group_of (read/write, boolean).
Whether the object represents a group of objects of the same class.is_depiction (read/write, boolean).
Whether the object is a depiction (such as a drawing)
rather than a real object.is_inside (read/write, boolean).
Whether the object is seen from the inside.Masks
box_id (read/write, string).
An identifier for the bounding box associated with the mask.predicted_iou (read/write, float).
Predicted IoU value with respect to the ground truth.The Open Images dataset is available for free download.
See the open-images-dataset GitHub repository
for information on how to download the images.
Datumaro also requires the image description files, which can be downloaded from the following URLs:
In addition, the following metadata file must be present
in the annotations directory:
You can optionally download the following additional metadata file:
Annotations can be downloaded from the following URLs:
All annotation files are optional, except that if the mask metadata files for a given subset are downloaded, all corresponding images must be downloaded as well, and vice versa.
A Datumaro project with an OID source can be created in the following way:
It is possible to specify project name and project directory. Run
datum create --help for more information.
Open Images dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of custom labels (optional)
├── annotations/
│ └── bbox_labels_600_hierarchy.json
│ └── image_ids_and_rotation.csv # optional
│ └── oidv6-class-descriptions.csv
│ └── *-annotations-bbox.csv
│ └── *-annotations-human-imagelabels.csv
│ └── *-annotations-object-segmentation.csv
├── images/
| ├── test/
| │ ├── <image_name1.jpg>
| │ ├── <image_name2.jpg>
| │ └── ...
| ├── train/
| │ ├── <image_name1.jpg>
| │ ├── <image_name2.jpg>
| │ └── ...
| └── validation/
| ├── <image_name1.jpg>
| ├── <image_name2.jpg>
| └── ...
└── masks/
├── test/
│ ├── <mask_name1.png>
│ ├── <mask_name2.png>
│ └── ...
├── train/
│ ├── <mask_name1.png>
│ ├── <mask_name2.png>
│ └── ...
└── validation/
├── <mask_name1.png>
├── <mask_name2.png>
└── ...
The mask images must be extracted from the ZIP archives linked above.
To use per-subset image description files instead of image_ids_and_rotation.csv,
place them in the annotations subdirectory.
To add custom classes, you can use dataset_meta.json.
To load bounding box and segmentation mask annotations, Datumaro needs to know the sizes of the corresponding images. By default, it will determine these sizes by loading each image from disk, which requires the images to be present and makes the loading process slow.
If you want to load the aforementioned annotations on a machine where
the images are not available,
or just to speed up the dataset loading process,
you can extract the image size information in advance
and record it in an image metadata file.
This file must be placed at annotations/images.meta,
and must contain one line per image, with the following structure:
<ID> <height> <width>
Where <ID> is the file name of the image without the extension,
and <height> and <width> are the dimensions of that image.
<ID> may be quoted with either single or double quotes.
The image metadata file, if present, will be used to determine the image sizes without loading the images themselves.
Here’s one way to create the images.meta file using ImageMagick,
assuming that the images are present on the current machine:
Datumaro can convert OID into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports image-level labels. There are several ways to convert OID to other dataset formats:
Or, using Python API:
There are several ways to convert an existing dataset to the Open Images format:
Extra options for exporting to the Open Images format:
--save-images - save image files when exporting the dataset
(by default, False)--image-ext IMAGE_EXT - save image files with the specified extension
when exporting the dataset (by default, uses the original extension
or .jpg if there isn’t one)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)Datumaro supports filtering, transformation, merging etc. for all formats and for the Open Images format in particular. Follow the user manual to get more information about these operations.
Here are a few examples of using Datumaro operations to solve particular problems with the Open Images dataset:
Examples of using this format from the code can be found in the format tests.
ImageNet is one of the most popular datasets for image classification task, this dataset is available for downloading here
Supported types of annotations:
LabelFormat doesn’t support any attributes for annotations objects.
The original ImageNet dataset contains about 1.2M images and information
about class name for each image. Datumaro supports two versions of ImageNet
format: imagenet and imagenet_txt. The imagenet_txt format assumes storing
information about the class of the image in *.txt files. And imagenet format
assumes storing information about the class of the image in the name of
directory where is this image stored.
A Datumaro project with a ImageNet dataset can be created in the following way:
datum create
datum import -f imagenet <path_to_dataset>
# or
datum import -f imagenet_txt <path_to_dataset>
Note: if you use
datum importthen <path_to_dataset> should not be a subdirectory of directory with Datumaro project, see more information about it in the docs.
Load ImageNet dataset through the Python API:
For successful importing of ImageNet dataset the input directory with dataset should has the following structure:
Note: if you don’t have synsets file then Datumaro will automatically generate classes with a name pattern
class-<i>.
Datumaro has few import options for imagenet_txt format, to apply them
use the -- after the main command argument.
imagenet_txt import options:
--labels {file, generate}: allow to specify where to get label
descriptions from (use file to load from the file specified
by --labels-file; generate to create generic ones)--labels-file allow to specify path to the file with label descriptions
(“synsets.txt”)Datumaro can convert ImageNet into any other format
Datumaro supports.
To get the expected result, convert the dataset to a format
that supports Label annotation objects.
# Using `convert` command
datum convert -if imagenet -i <path_to_imagenet> \
-f voc -o <output_dir> -- --save-images
# Using Datumaro project
datum create
datum import -f imagenet_txt <path_to_imagenet> -- --labels generate
datum export -f open_images -o <output_dir>
And also you can convert your ImageNet dataset using Python API
Note: some formats have extra export options. For particular format see the docs to get information about it.
If your dataset contains Label for images and you want to convert this
dataset into the ImagetNet format, you can use Datumaro for it:
# Using convert command
datum convert -if open_images -i <path_to_oid> \
-f imagenet_txt -o <output_dir> -- --save-images --save-dataset-meta
# Using Datumaro project
datum create
datum import -f open_images <path_to_oid>
datum export -f imagenet -o <output_dir>
Extra options for exporting to ImageNet formats:
--save-images allow to export dataset with saving images
(by default False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting the dataset (by default .png)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)Pascal VOC format specification is available here.
The dataset has annotations for multiple tasks. Each task has its own format
in Datumaro, and there is also a combined voc format, which includes all
the available tasks. The sub-formats have the same options as the “main”
format and only limit the set of annotation files they work with. To work with
multiple formats, use the corresponding option of the voc format.
Supported tasks / formats:
vocvoc_classificationvoc_detectionvoc_actionvoc_segmentationvoc_layoutSupported annotation types:
Label (classification)Bbox (detection, action detection and person layout)Mask (segmentation)Supported annotation attributes:
occluded (boolean) - indicates that a significant portion of the
object within the bounding box is occluded by another objecttruncated (boolean) - indicates that the bounding box specified for
the object does not correspond to the full extent of the objectdifficult (boolean) - indicates that the object is considered difficult
to recognizejumping, reading and
others.
Indicate that the object does the corresponding action.attributes section of the annotation xml file. Available for
bbox annotations only.The Pascal VOC dataset is available for free download here
A Datumaro project with a Pascal VOC source can be created in the following way:
It is possible to specify project name and project directory. Run
datum create --help for more information.
Pascal VOC dataset directory should have the following structure:
└─ Dataset/
├── dataset_meta.json # a list of non-Pascal labels (optional)
├── labelmap.txt # or a list of non-Pascal labels in other format (optional)
│
├── Annotations/
│ ├── ann1.xml # Pascal VOC format annotation file
│ ├── ann2.xml
│ └── ...
├── JPEGImages/
│ ├── img1.jpg
│ ├── img2.jpg
│ └── ...
├── SegmentationClass/ # directory with semantic segmentation masks
│ ├── img1.png
│ ├── img2.png
│ └── ...
├── SegmentationObject/ # directory with instance segmentation masks
│ ├── img1.png
│ ├── img2.png
│ └── ...
│
└── ImageSets/
├── Main/ # directory with list of images for detection and classification task
│ ├── test.txt # list of image names in test subset (without extension)
| ├── train.txt # list of image names in train subset (without extension)
| └── ...
├── Layout/ # directory with list of images for person layout task
│ ├── test.txt
| ├── train.txt
| └── ...
├── Action/ # directory with list of images for action classification task
│ ├── test.txt
| ├── train.txt
| └── ...
└── Segmentation/ # directory with list of images for segmentation task
├── test.txt
├── train.txt
└── ...
The ImageSets directory should contain at least one of the directories:
Main, Layout, Action, Segmentation.
These directories contain .txt files with a list of images in a subset,
the subset name is the same as the .txt file name. Subset names can be
arbitrary.
To add custom classes, you can use dataset_meta.json
and labelmap.txt.
If the dataset_meta.json is not represented in the dataset, then
labelmap.txt will be imported if possible.
In labelmap.txt you can define custom color map and non-pascal labels,
for example:
# label_map [label : color_rgb : parts : actions]
helicopter:::
elephant:0:124:134:head,ear,foot:
It is also possible to import grayscale (1-channel) PNG masks. For grayscale masks provide a list of labels with the number of lines equal to the maximum color index on images. The lines must be in the right order so that line index is equal to the color index. Lines can have arbitrary, but different, colors. If there are gaps in the used color indices in the annotations, they must be filled with arbitrary dummy labels. Example:
car:0,128,0:: # color index 0
aeroplane:10,10,128:: # color index 1
_dummy2:2,2,2:: # filler for color index 2
_dummy3:3,3,3:: # filler for color index 3
boat:108,0,100:: # color index 3
...
_dummy198:198,198,198:: # filler for color index 198
_dummy199:199,199,199:: # filler for color index 199
the_last_label:12,28,0:: # color index 200
You can import dataset for specific tasks of Pascal VOC dataset instead of the whole dataset, for example:
To make sure that the selected dataset has been added to the project, you
can run datum project info, which will display the project information.
Datumaro can convert a Pascal VOC dataset into any other format Datumaro supports.
Such conversion will only be successful if the output
format can represent the type of dataset you want to convert,
e.g. image classification annotations can be
saved in ImageNet format, but not as COCO keypoints.
There are several ways to convert a Pascal VOC dataset to other dataset formats:
Or, using Python API:
There are several ways to convert an existing dataset to Pascal VOC format:
Extra options for exporting to Pascal VOC format:
--save-images - allow to export dataset with saving images
(by default False)--image-ext IMAGE_EXT - allow to specify image extension
for exporting dataset (by default use original or .jpg if none)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)--apply-colormap APPLY_COLORMAP - allow to use colormap for class
and instance masks (by default True)--allow-attributes ALLOW_ATTRIBUTES - allow export of attributes
(by default True)--keep-empty KEEP_EMPTY - write subset lists even if they are empty
(by default False)--tasks TASKS - allow to specify tasks for export dataset,
by default Datumaro uses all tasks. Example:--label_map PATH - allows to define a custom colormap. Example:Datumaro supports filtering, transformation, merging etc. for all formats and for the Pascal VOC format in particular. Follow user manual to get more information about these operations.
There are few examples of using Datumaro operations to solve particular problems with Pascal VOC dataset:
In this example, preparing the original dataset to train the semantic segmentation model includes: loading, checking duplicate images, setting the number of images, splitting into subsets, export the result to Pascal VOC format.
Load Pascal VOC dataset, and export train subset with items
which has jumping attribute:
After executing this code, we can see that there are 5826 images in Pascal VOC 2012 has for segmentation task and this result is the same as the official documentation
Examples of using this format from the code can be found in tests
Specification for the Point Cloud data format is available here.
You can also find examples of working with the dataset here.
Supported annotation types:
cuboid_3dSupported annotation attributes:
track_id (read/write, integer), responsible for object fieldcreatedAt (write, string),updatedAt (write, string),labelerLogin (write, string), responsible for the corresponding fields
in the annotation file.Supported image attributes:
description (read/write, string),createdAt (write, string),updatedAt (write, string),labelerLogin (write, string), responsible for the corresponding fields
in the annotation file.frame (read/write, integer). Indicates frame number of the image.An example dataset in Supervisely Point Cloud format is available for download:
https://drive.google.com/u/0/uc?id=1BtZyffWtWNR-mk_PHNPMnGgSlAkkQpBl&export=download
Point Cloud dataset directory should have the following structure:
└─ Dataset/
├── ds0/
│ ├── ann/
│ │ ├── <pcdname1.pcd.json>
│ │ ├── <pcdname2.pcd.json>
│ │ └── ...
│ ├── pointcloud/
│ │ ├── <pcdname1.pcd>
│ │ ├── <pcdname1.pcd>
│ │ └── ...
│ ├── related_images/
│ │ ├── <pcdname1_pcd>/
│ │ | ├── <image_name.ext.json>
│ │ | ├── <image_name.ext.json>
│ │ └── ...
├── key_id_map.json
└── meta.json
There are two ways to import a Supervisely Point Cloud dataset:
To make sure that the selected dataset has been added to the project,
you can run datum project info, which will display the project and dataset
information.
Datumaro can convert Supervisely Point Cloud dataset into any other format Datumaro supports.
Such conversion will only be successful if the output
format can represent the type of dataset you want to convert,
e.g. 3D point clouds can be saved in KITTI Raw format,
but not in COCO keypoints.
There are several ways to convert a Supervisely Point Cloud dataset to other dataset formats:
Or, using Python API:
There are several ways to convert a dataset to Supervisely Point Cloud format:
Extra options for exporting in Supervisely Point Cloud format:
--save-images allow to export dataset with saving images. This will
include point clouds and related images (by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original or use .png, if none)--reindex assigns new indices to frames and annotations.--allow-undeclared-attrs allows writing arbitrary annotation attributes.
By default, only attributes specified in the input dataset metainfo
will be written.Examples of using this format from the code can be found in the format tests
The YOLO dataset format is for training and validating object detection models. Specification for this format is available here.
You can also find official examples of working with YOLO dataset here.
Supported annotation types:
Bounding boxesYOLO format doesn’t support attributes for annotations.
The format only supports subsets named train or valid.
A Datumaro project with a YOLO source can be created in the following way:
YOLO dataset directory should have the following structure:
└─ yolo_dataset/
│
├── dataset_meta.json # a list of non-format labels (optional)
├── obj.names # file with list of classes
├── obj.data # file with dataset information
├── train.txt # list of image paths in train subset
├── valid.txt # list of image paths in valid subset
│
├── obj_train_data/ # directory with annotations and images for train subset
│ ├── image1.txt # list of labeled bounding boxes for image1
│ ├── image1.jpg
│ ├── image2.txt
│ ├── image2.jpg
│ └── ...
│
└── obj_valid_data/ # directory with annotations and images for valid subset
├── image101.txt
├── image101.jpg
├── image102.txt
├── image102.jpg
└── ...
YOLO dataset cannot contain a subset with a name other than
trainorvalid. If an imported dataset contains such subsets, they will be ignored. If you are exporting a project into YOLO format, all subsets different fromtrainandvalidwill be skipped. If there is no subset separation in a project, the data will be saved intrainsubset.
obj.data should have the following content, it is not necessary to have both
subsets, but necessary to have one of them:classes = 5 # optional
names = <path/to/obj.names>
train = <path/to/train.txt>
valid = <path/to/valid.txt>
backup = backup/ # optional
obj.names contains a list of classes.
The line number for the class is the same as its index:label1 # label1 has index 0
label2 # label2 has index 1
label3 # label2 has index 2
...
train.txt and valid.txt should have the following structure:<path/to/image1.jpg>
<path/to/image2.jpg>
...
obj_train_data/ and obj_valid_data/
should contain information about labeled bounding boxes
for images:# image1.txt:
# <label_index> <x_center> <y_center> <width> <height>
0 0.250000 0.400000 0.300000 0.400000
3 0.600000 0.400000 0.400000 0.266667
Here x_center, y_center, width, and height are relative to the image’s
width and height. The x_center and y_center are center of rectangle
(are not top-left corner).
To add custom classes, you can use dataset_meta.json.
Datumaro can convert YOLO dataset into any other format Datumaro supports. For successful conversion the output format should support object detection task (e.g. Pascal VOC, COCO, TF Detection API etc.)
There are several ways to convert a YOLO dataset to other dataset formats:
Or, using Python API:
Datumaro can convert an existing dataset to YOLO format, if the dataset supports object detection task.
Example:
datum create
datum import -f coco_instances <path/to/dataset>
datum export -f yolo -o <path/to/dataset> -- --save-images
Extra options for exporting to YOLO format:
--save-images allow to export dataset with saving images
(default: False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting dataset (default: use original or .jpg, if none)Delete all items, which contain cat objects and remove
cat from list of classes:
If you only want information about label names for each image, then you can get it from code:
And If you want complete information about each item you can run:
Market-1501 is a dataset for person re-identification task, link for downloading this dataset is available here.
Supported items attributes:
person_id (str): four-digit number that represent ID of pedestrian;camera_id (int): one-digit number that represent ID of camera that took
the image (original dataset has totally 6 cameras);track_id (int): one-digit number that represent ID of the track with
the particular pedestrian, this attribute matches with sequence_id
in the original dataset;frame_id (int): six-digit number, that mean number of
frame within this track. For the tracks, their names are accumulated
for each ID, but for frames, they start from “0001” in each track;bbox_id (int): two-digit number, that mean number of
bounding bbox that was selected for that image
(see the
original docs
for more info).These item attributes decodes into the image name with such convention:
0000_c1s1_000000_00.jpg
person_id;c indicates the camera_id;s indicate the track_id;s1_ indicate the frame_id;.jpg indicate the bbox_id.Importing of Market-1501 dataset into the Datumaro project:
datum create
datum import -f market1501 <path_to_market1501>
See more information about adding datasets to the project in the docs.
Or you can import Market-1501 using Python API:
For successful importing the Market-1501 dataset, the directory with it should has the following structure:
market1501_dataset/
├── query # optional directory with query image
│ ├── 0001_c1s1_001051_00.jpg
│ ├── 0002_c1s1_001051_00.jpg
│ ├── ...
├── bounding_box_<subset_name1>
│ ├── 0003_c1s1_001051_00.jpg
│ ├── 0003_c2s1_001054_01.jpg
│ ├── 0004_c1s1_001051_00.jpg
│ ├── ...
├── bounding_box_<subset_name2>
│ ├── 0005_c1s1_001051_00.jpg
│ ├── 0006_c1s1_001051_00.jpg
│ ├── ...
├── ...
With Datumaro you can export dataset, that has person_id item attribute,
to the Market-1501 format, example:
# Converting MARS dataset into the Market-1501
datum convert -if mars -i ./mars_dataset \
-f market1501 -o ./output_dir
# Export dataaset to the Market-1501 format through the Datumaro project:
datum create
datum add -f mars ../mars
datum export -f market1501 -o ./output_dir -- --save-images --image-ext png
Note: if your dataset contains only person_id attributes Datumaro will assign default values for other attributes (camera_id, track_id, bbox_id) and increment frame_id for collisions.
Available extra export options for Market-1501 dataset format:
--save-images allow to export dataset with saving images.
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original)MARS is a dataset for the motion analysis and person identification task. MARS dataset is available for downloading here
Supported types of annotations:
BboxRequired attributes:
person_id (str): four-digit number that represent ID of pedestrian;camera_id (int): one-digit number that represent ID of camera that took
the image (original dataset has totally 6 cameras);track_id (int): four-digit number that represent ID of the track with
the particular pedestrian;frame_id (int): three-digit number, that mean number of
frame within this track. For the tracks, their names are accumulated
for each ID, but for frames, they start from “0001” in each track.Use these instructions to import MARS dataset into Datumaro project:
Note: the directory with dataset should be subdirectory of the project directory.
mars_dataset
├── <bbox_subset_name1>
│ ├── 0001 # directory with images of pedestrian with id 0001
│ │ ├── 0001C1T0001F001.jpg
│ │ ├── 0001C1T0001F002.jpg
│ │ ├── ...
│ ├── 0002 # directory with images of pedestrian with id 0002
│ │ ├── 0002C1T0001F001.jpg
│ │ ├── 0002C1T0001F001.jpg
│ │ ├── ...
│ ├── 0000 # distractors images, which negatively affect retrieval accuracy.
│ │ ├── 0000C1T0001F001.jpg
│ │ ├── 0000C1T0001F001.jpg
│ │ ├── ...
│ ├── 00-1 # junk images which do not affect retrieval accuracy
│ │ ├── 00-1C1T0001F001.jpg
│ │ ├── 00-1C1T0001F001.jpg
│ │ ├── ...
├── <bbox_subset_name2>
│ ├── ...
├── ...
All images in MARS dataset follow a strict convention of naming:
xxxxCxTxxxxFxxx.jpg
C indicates the camera id;T indicate the track id for this pedestrian;F indicate the frame id with this track.Note: there are two specific pedestrian IDs 0000 and 00-1 which indicate distracting images and unwanted images respectively.
WIDER Face dataset is a face detection benchmark dataset, that available for download here.
Supported types of annotation:
BboxLabelSupported attributes for bboxes:
blur:
expression:
illumination:
pose:
invalid:
occluded:
Importing of WIDER Face dataset into the Datumaro project:
datum create
datum import -f wider_face <path_to_wider_face>
Directory with WIDER Face dataset should has the following structure:
<path_to_wider_face>
├── labels.txt # optional file with list of classes
├── wider_face_split # directory with description of bboxes for each image
│ ├── wider_face_subset1_bbx_gt.txt
│ ├── wider_face_subset2_bbx_gt.txt
│ ├── ...
├── WIDER_subset1 # instead of 'subset1' you can use any other subset name
│ └── images
│ ├── 0--label_0 # instead of 'label_<n>' you can use any other class name
│ │ ├── 0_label_0_image_01.jpg
│ │ ├── 0_label_0_image_02.jpg
│ │ ├── ...
│ ├── 1--label_1
│ │ ├── 1_label_1_image_01.jpg
│ │ ├── 1_label_1_image_02.jpg
│ │ ├── ...
│ ├── ...
├── WIDER_subset2
│ └── images
│ ├── ...
├── ...
Check README
file of the original WIDER Face dataset to get more information
about structure of .txt annotation files.
Also example of WIDER Face dataset available in our
test assets.
With Datumaro you can convert WIDER Face dataset into any other
format Datumaro supports.
Pay attention that this format should also support Label and/or Bbox
annotation types.
Few ways to export WIDER Face dataset using CLI:
# Using `convert` command
datum convert -if wider_face -i <path_to_wider_face> \
-f voc -o <output_dir> -- --save-images
# Through the Datumaro project
datum create
datum import -f wider_face <path_to_wider_face>
datum export -f voc -o <output_dir> -- -save-images
Export WIDER Face dataset using Python API:
Note: some formats have extra export options. For particular format see the docs to get information about it.
Using Datumaro you can convert your dataset into the WIDER Face format,
but for succseful exporting your dataset should contain Label and/or Bbox.
Here example of exporting VOC dataset (object detection task) into the WIDER Face format:
datum create
datum import -f voc_detection <path_to_voc>
datum export -f wider_face -o <output_dir> -- --save-images --image-ext='.png'
Available extra export options for WIDER Face dataset format:
--save-images allow to export dataset with saving images.
(by default False)--image-ext IMAGE_EXT allow to specify image extension
for exporting dataset (by default - keep original)Vgg Face 2 is a dataset for face-recognition task, the repository with some information and sample data of Vgg Face 2 is available here
Supported types of annotations:
BboxPointsLabelFormat doesn’t support any attributes for annotations objects.
A Datumaro project with a Vgg Face 2 dataset can be created in the following way:
datum create
datum import -f vgg_face2 <path_to_dataset>
Note: if you use
datum importthen <path_to_dataset> should not be a subdirectory of directory with Datumaro project, see more information about it in the docs.
And you can also load Vgg Face 2 through the Python API:
For successful importing of Vgg Face2 face the input directory with dataset should has the following structure:
vgg_face2_dataset/
├── labels.txt # labels mapping
├── bb_landmark
│ ├── loose_bb_test.csv # information about bounding boxes for test subset
│ ├── loose_bb_train.csv
│ ├── loose_bb_<any_other_subset_name>.csv
│ ├── loose_landmark_test.csv # landmark points information for test subset
│ ├── loose_landmark_train.csv
│ └── loose_landmark_<any_other_subset_name>.csv
├── test
│ ├── n000001 # directory with images for n000001 label
│ │ ├── 0001_01.jpg
│ │ ├── 0001_02.jpg
│ │ ├── ...
│ ├── n000002 # directory with images for n000002 label
│ │ ├── 0002_01.jpg
│ │ ├── 0003_01.jpg
│ │ ├── ...
│ ├── ...
├── train
│ ├── n000004
│ │ ├── 0004_01.jpg
│ │ ├── 0004_02.jpg
│ │ ├── ...
│ ├── ...
└── <any_other_subset_name>
├── ...
Datumaro can convert a Vgg Face2 dataset into any other format Datumaro supports. There is few examples how to do it:
# Using `convert` command
datum convert -if vgg_face2 -i <path_to_vgg_face2> \
-f voc -o <output_dir> -- --save-images
# Using Datumaro project
datum create
datum import -f vgg_face2 <path_to_vgg_face2>
datum export -f yolo -o <output_dir>
Note: to get the expected result from the conversion, the output format should support the same types of annotations (one or more) as Vgg Face2 (
Bbox,Points,Label)
And also you can convert your Vgg Face2 dataset using Python API
Note: some formats have extra export options. For particular format see the docs to get information about it.
If you have dataset in some format and want to convert this dataset
into the Vgg Face2, ensure that this dataset contains Bbox or/and Points
or/and Label and use Datumaro to perform conversion.
There is few examples:
# Using convert command
datum convert -if wider_face -i <path_to_wider> \
-f vgg_face2 -o <output_dir>
# Using Datumaro project
datum create
datum import -f wider_face <path_to_wider>
datum export -f vgg_face2 -o <output_dir> -- --save-images --image-ext '.png'
Note:
vgg_face2format supports only oneBboxper image
Extra options for exporting to Vgg Face2 format:
--save-images allow to export dataset with saving images
(by default False)--image-ext <IMAGE_EXT> allow to specify image extension
for exporting the dataset (by default .png)--save-dataset-meta - allow to export dataset with saving dataset meta
file (by default False)There are detection and image classification examples.
Detection (SSD-based)
Intel Pre-trained Models > Object Detection
Public Pre-Trained Models(OMZ) > Object Detection
Image Classification
You can find more OpenVINO™ Trained Models here To run the inference with OpenVINO™, the model format should be Intermediate Representation(IR). For the Caffe/TensorFlow/MXNet/Kaldi/ONNX models, please see the Model Conversion Instruction
You need to implement your own interpreter samples to support the other OpenVINO™ Trained Models.
Prerequisites:
Open Model Zoo models can be downloaded with the Model Downloader tool from OpenVINO™ distribution:
Example: download the “face-detection-0200” model
Prerequisites:
To run the inference with OpenVINO™ models and the interpreter samples, please follow the instructions below.
Optionally, install a virtual environment (recommended):
Then install all dependencies:
Install Datumaro:
Optional dependencies
These components are only required for plugins and not installed by default:
Try to be readable and consistent with the existing codebase.
The project mostly follows PEP8 with little differences. Continuation lines have a standard indentation step by default, or any other, if it improves readability. For long conditionals use 2 steps. No trailing whitespaces, 80 characters per line.
Example:
The recommended editor is VS Code with the Python language plugin.
It is expected that all Datumaro functionality is covered and checked by
unit tests. Tests are placed in the tests/ directory. Additional
pre-generated files for tests can be stored in the tests/assets/ directory.
CLI tests are separated from the core tests, they are stored in the
tests/cli/ directory.
Currently, we use pytest for testing.
To run tests use:
For better integration with CI and requirements tracking, we use special annotations for tests.
A test needs to linked with a requirement it is related to. To link a test, use:
Such marking will apply markings from the requirement specified. They can be overridden for a specific test:
Requirements and other links need to be added to tests/requirements.py:
Markings are defined in tests/conftest.py.
A list of requirements and bugs
A priority
Component The marking used for indication of different system components
Skipping tests
Parametrized runs
Parameters are used for running the same test with different parameters e.g.
Tests are documented with docs strings. Test descriptions must contain
the following: sections: Description, Expected results and Steps.
Notes about the release of the developed version can be read in the CHANGELOG.md of the develop branch.