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Datumaro 1.11.1 documentation - Home
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Get Started

  • Introduction
  • Quick Start Guide
    • Installation
    • Usage
    • Examples

Level Up

  • Basic Skills
    • Level 1: Dataset download
    • Level 2: Data Import and Export
    • Level 3: Detect Data Format from an Unknown Dataset
  • Intermediate Skills
    • Level 4: Data Subset Aggregation
    • Level 6: Data Comparison with Two Heterogeneous Datasets
    • Level 6: Merge Two Heterogeneous Datasets
    • Level 7: Dataset Validation
    • Level 8: Dataset Filtering
    • Level 9: Framework Conversion

Data Formats

  • Formats
    • ADE20k (v2017)
    • ADE20k (v2020)
    • Align CelebA
    • Arrow
    • AVA Action
    • BraTS
    • BraTS Numpy
    • CelebA
    • CIFAR
    • Cityscapes
    • COCO
    • CVAT
    • Datumaro
    • DatumaroBinary
    • DOTA
    • ICDAR
    • Image zip
    • ImageNet
    • Kaggle
    • Kinetics
    • KITTI
    • Velodyne Points / KITTI Raw 3D
    • LabelMe
    • LFW
    • Mapillary Vistas
    • Market-1501
    • MARS
    • MMDetection COCO
    • MNIST
    • Multiple Object Tracking (MOT)
    • Multiple Object Tracking and Segmentation (MOTS)
    • MPII Human Pose
    • MPII Human Pose JSON
    • MVTec AD
    • NYU Depth Dataset V2
    • Open Images
    • Pascal VOC
    • Roboflow
    • Segment Anything
    • Supervisely Point Cloud
    • SYNTHIA
    • Tabular
    • Vgg Face2 CSV
    • Video
    • VoTT CSV
    • VoTT JSON
    • WIDER Face
    • YOLO
    • YOLO-Ultralytics
  • Supported Media Formats
  • Datumaro Format

Hands-on Examples

  • Dataset I/O
    • Import and Export Public Data
    • Import and Export Public Semantic Segmentation Data
  • Manipulate
    • Merge Multiple Datasets for Classification Tasks
    • Merge Heterogeneous Datasets for Detection
  • Refine
    • Validate Dataset To Detect Anomalies
    • Correct Dataset from Validation Report
    • Filter Data through Your Query
  • Transform
    • Transform Dataset: Re-id, Reindexing, Remapping, etc.
    • Tile your Dataset to Cope with High-Resolution Images
  • From Datumaro to Model Training
    • Train Your OpenVINO™ Model Using YoloV8 Trainer For Any Dataset Format
    • Data Framework Convert

Command Line Reference

  • Overview
  • Compare
  • Convert
  • Detect
  • Info
  • Download
  • Filter
  • Format
  • Merge
  • Patch
  • Stats
  • Transform
  • Util
  • Validate

API Reference

  • Datumaro Module
    • datumaro
      • datumaro.cli
        • datumaro.cli.commands
        • datumaro.cli.contexts
        • datumaro.cli.helpers
        • datumaro.cli.util
      • datumaro.components
        • datumaro.components.abstracts
        • datumaro.components.algorithms
        • datumaro.components.annotation
        • datumaro.components.annotations
        • datumaro.components.cli_plugin
        • datumaro.components.comparator
        • datumaro.components.config
        • datumaro.components.config_model
        • datumaro.components.contexts
        • datumaro.components.dataset
        • datumaro.components.dataset_base
        • datumaro.components.dataset_item_storage
        • datumaro.components.dataset_storage
        • datumaro.components.environment
        • datumaro.components.errors
        • datumaro.components.exporter
        • datumaro.components.extractor_tfds
        • datumaro.components.filter
        • datumaro.components.format_detection
        • datumaro.components.generator
        • datumaro.components.hl_ops
        • datumaro.components.importer
        • datumaro.components.lazy_plugin
        • datumaro.components.media
        • datumaro.components.merge
        • datumaro.components.operations
        • datumaro.components.progress_reporting
        • datumaro.components.registry
        • datumaro.components.transformer
        • datumaro.components.validator
        • datumaro.components.visualizer
      • datumaro.errors
      • datumaro.experimental
        • datumaro.experimental.categories
        • datumaro.experimental.converter_registry
        • datumaro.experimental.converters
        • datumaro.experimental.dataset
        • datumaro.experimental.fields
        • datumaro.experimental.legacy
        • datumaro.experimental.schema
        • datumaro.experimental.type_registry
      • datumaro.ops
      • datumaro.plugins
        • datumaro.plugins.configurable_validator
        • datumaro.plugins.data_formats
        • datumaro.plugins.framework_converter
        • datumaro.plugins.ndr
        • datumaro.plugins.sampler
        • datumaro.plugins.specs
        • datumaro.plugins.splitter
        • datumaro.plugins.tiling
        • datumaro.plugins.transforms
        • datumaro.plugins.validators
      • datumaro.rust_api
      • datumaro.util
        • datumaro.util.annotation_util
        • datumaro.util.attrs_util
        • datumaro.util.definitions
        • datumaro.util.deprecation
        • datumaro.util.file_utils
        • datumaro.util.image
        • datumaro.util.image_cache
        • datumaro.util.import_util
        • datumaro.util.log_utils
        • datumaro.util.mask_tools
        • datumaro.util.meta_file_util
        • datumaro.util.multi_procs_util
        • datumaro.util.os_util
        • datumaro.util.pickle_util
        • datumaro.util.points_util
        • datumaro.util.scope
        • datumaro.util.tabular_util
        • datumaro.util.tf_util
      • datumaro.version
  • Supported Plugins

Explanation

  • Concepts
  • Architecture

Misc

  • How to use Datumaro
  • Model Preparation
  • Extending

Release Notes

  • Release Notes
  • Docs
  • Manipulate

Manipulate#

When importing multiple datasets, Datumaro helps to manipulate and merge them into a single dataset. We here describe how to merge two heterogeneous datasets through IntersectMerge.

Merge Multiple Datasets for Classification Tasks

Merge Heterogeneous Datasets for Detection

previous

Import and Export Public Semantic Segmentation Data

next

Merge Multiple Datasets for Classification Tasks

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