diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9c87c68660..510dc94255 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,10 +16,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/openvinotoolkit/datumaro/pull/547>)
 - Import for SYNTHIA dataset format
   (<https://github.com/openvinotoolkit/datumaro/pull/532>)
-- Support for Accuracy Checker dataset meta files in formats
-  (<https://github.com/openvinotoolkit/datumaro/pull/553>)
 - Support of `score` attribute in KITTI detetion
   (<https://github.com/openvinotoolkit/datumaro/pull/571>)
+- Support for Accuracy Checker dataset meta files in formats
+  (<https://github.com/openvinotoolkit/datumaro/pull/553>,
+  <https://github.com/openvinotoolkit/datumaro/pull/569>)
 
 ### Changed
 - The following formats can now be detected unambiguously:
diff --git a/datumaro/plugins/ade20k2017_format.py b/datumaro/plugins/ade20k2017_format.py
index 25ab8f177e..0bcffa27f4 100644
--- a/datumaro/plugins/ade20k2017_format.py
+++ b/datumaro/plugins/ade20k2017_format.py
@@ -18,6 +18,7 @@
 from datumaro.util.image import (
     IMAGE_EXTENSIONS, find_images, lazy_image, load_image,
 )
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class Ade20k2017Path:
@@ -31,7 +32,9 @@ def __init__(self, path):
         if not osp.isdir(path):
             raise FileNotFoundError("Can't read dataset directory '%s'" % path)
 
-        subsets = os.listdir(path)
+        # exclude dataset meta file
+        subsets = [subset for subset in os.listdir(path)
+            if osp.splitext(subset)[-1] != '.json']
         if len(subsets) < 1:
             raise FileNotFoundError("Can't read subsets in directory '%s'" % path)
 
@@ -41,6 +44,10 @@ def __init__(self, path):
         self._items = []
         self._categories  = {}
 
+        if has_meta_file(self._path):
+            self._categories =  { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(self._path).keys())) }
+
         for subset in self._subsets:
             self._load_items(subset)
 
diff --git a/datumaro/plugins/ade20k2020_format.py b/datumaro/plugins/ade20k2020_format.py
index fa20c48dd5..38c4b5317d 100644
--- a/datumaro/plugins/ade20k2020_format.py
+++ b/datumaro/plugins/ade20k2020_format.py
@@ -19,6 +19,7 @@
 from datumaro.util.image import (
     IMAGE_EXTENSIONS, find_images, lazy_image, load_image,
 )
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class Ade20k2020Path:
@@ -33,7 +34,9 @@ def __init__(self, path):
         if not osp.isdir(path):
             raise FileNotFoundError("Can't read dataset directory '%s'" % path)
 
-        subsets = os.listdir(path)
+        # exclude dataset meta file
+        subsets = [subset for subset in os.listdir(path)
+            if osp.splitext(subset)[-1] != '.json']
         if len(subsets) < 1:
             raise FileNotFoundError("Can't read subsets in directory '%s'" % path)
 
@@ -43,6 +46,10 @@ def __init__(self, path):
         self._items = []
         self._categories  = {}
 
+        if has_meta_file(self._path):
+            self._categories =  { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(self._path).keys())) }
+
         for subset in self._subsets:
             self._load_items(subset)
 
diff --git a/datumaro/plugins/align_celeba_format.py b/datumaro/plugins/align_celeba_format.py
index 1e3a6f0ebc..6a51aae885 100644
--- a/datumaro/plugins/align_celeba_format.py
+++ b/datumaro/plugins/align_celeba_format.py
@@ -10,6 +10,7 @@
 from datumaro.components.errors import DatasetImportError
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
 from datumaro.util.image import find_images
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class AlignCelebaPath:
@@ -32,6 +33,10 @@ def __init__(self, path):
         self._anno_dir = osp.dirname(path)
 
         self._categories = { AnnotationType.label: LabelCategories() }
+        if has_meta_file(path):
+            self._categories = { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(path).keys())) }
+
         self._items = list(self._load_items(path).values())
 
     def _load_items(self, root_dir):
diff --git a/datumaro/plugins/celeba_format.py b/datumaro/plugins/celeba_format.py
index d7624b5dee..639a7c7b5a 100644
--- a/datumaro/plugins/celeba_format.py
+++ b/datumaro/plugins/celeba_format.py
@@ -10,6 +10,7 @@
 from datumaro.components.errors import DatasetImportError
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
 from datumaro.util.image import find_images
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class CelebaPath:
@@ -30,6 +31,10 @@ def __init__(self, path):
         super().__init__()
 
         self._categories = { AnnotationType.label: LabelCategories() }
+        if has_meta_file(path):
+            self._categories = { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(path).keys())) }
+
         self._items = list(self._load_items(path).values())
 
     def _load_items(self, root_dir):
diff --git a/datumaro/plugins/cifar_format.py b/datumaro/plugins/cifar_format.py
index 8ed8ead22c..f372f1304a 100644
--- a/datumaro/plugins/cifar_format.py
+++ b/datumaro/plugins/cifar_format.py
@@ -17,6 +17,7 @@
 from datumaro.components.dataset import ItemStatus
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
 from datumaro.util import cast
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class RestrictedUnpickler(pickle.Unpickler):
@@ -65,6 +66,10 @@ def __init__(self, path, subset=None):
         self._items = list(self._load_items(path).values())
 
     def _load_categories(self, path):
+        if has_meta_file(path):
+            return { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(path).keys())) }
+
         label_cat = LabelCategories()
 
         meta_file = osp.join(path, CifarPath.META_10_FILE)
@@ -175,6 +180,9 @@ class CifarConverter(Converter):
     def apply(self):
         os.makedirs(self._save_dir, exist_ok=True)
 
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+
         label_categories = self._extractor.categories()[AnnotationType.label]
         label_names = []
         coarse_label_names = []
diff --git a/datumaro/plugins/kitti_format/converter.py b/datumaro/plugins/kitti_format/converter.py
index ae7b0e821e..df187a8c6c 100644
--- a/datumaro/plugins/kitti_format/converter.py
+++ b/datumaro/plugins/kitti_format/converter.py
@@ -18,6 +18,7 @@
 from datumaro.util.annotation_util import make_label_id_mapping
 from datumaro.util.image import save_image
 from datumaro.util.mask_tools import paint_mask
+from datumaro.util.meta_file_util import is_meta_file, parse_meta_file
 
 from .format import (
     KittiLabelMap, KittiPath, KittiTask, make_kitti_categories, parse_label_map,
@@ -156,8 +157,11 @@ def get_label(self, label_id):
             categories()[AnnotationType.label].items[label_id].name
 
     def save_label_map(self):
-        path = osp.join(self._save_dir, KittiPath.LABELMAP_FILE)
-        write_label_map(path, self._label_map)
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+        else:
+            path = osp.join(self._save_dir, KittiPath.LABELMAP_FILE)
+            write_label_map(path, self._label_map)
 
     def _load_categories(self, label_map_source):
         if label_map_source == LabelmapType.kitti.name:
@@ -188,7 +192,10 @@ def _load_categories(self, label_map_source):
                 sorted(label_map_source.items(), key=lambda e: e[0]))
 
         elif isinstance(label_map_source, str) and osp.isfile(label_map_source):
-            label_map = parse_label_map(label_map_source)
+            if is_meta_file(label_map_source):
+                label_map = parse_meta_file(label_map_source)
+            else:
+                label_map = parse_label_map(label_map_source)
 
         else:
             raise Exception("Wrong labelmap specified, "
diff --git a/datumaro/plugins/kitti_format/extractor.py b/datumaro/plugins/kitti_format/extractor.py
index 7d216f4ffc..558e718b2f 100644
--- a/datumaro/plugins/kitti_format/extractor.py
+++ b/datumaro/plugins/kitti_format/extractor.py
@@ -12,6 +12,7 @@
     AnnotationType, DatasetItem, SourceExtractor,
 )
 from datumaro.util.image import find_images, load_image
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 from .format import (
     KittiLabelMap, KittiPath, KittiTask, make_kitti_categories, parse_label_map,
@@ -36,15 +37,23 @@ def _load_categories(self, path):
         if self._task == KittiTask.segmentation:
             return self._load_categories_segmentation(path)
         elif self._task == KittiTask.detection:
+            if has_meta_file(path):
+                return { AnnotationType.label: LabelCategories().
+                    from_iterable(list(parse_meta_file(path).keys())) }
+
             return {AnnotationType.label: LabelCategories()}
 
     def _load_categories_segmentation(self, path):
         label_map = None
-        label_map_path = osp.join(path, KittiPath.LABELMAP_FILE)
-        if osp.isfile(label_map_path):
-            label_map = parse_label_map(label_map_path)
+        if has_meta_file(path):
+            label_map = parse_meta_file(path)
         else:
-            label_map = KittiLabelMap
+            label_map_path = osp.join(path, KittiPath.LABELMAP_FILE)
+            if osp.isfile(label_map_path):
+                label_map = parse_label_map(label_map_path)
+            else:
+                label_map = KittiLabelMap
+
         self._labels = [label for label in label_map]
         return make_kitti_categories(label_map)
 
diff --git a/datumaro/plugins/lfw_format.py b/datumaro/plugins/lfw_format.py
index 63ab3148ee..4bd3724cce 100644
--- a/datumaro/plugins/lfw_format.py
+++ b/datumaro/plugins/lfw_format.py
@@ -13,6 +13,7 @@
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
 from datumaro.components.format_detection import FormatDetectionContext
 from datumaro.util.image import find_images
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class LfwPath:
@@ -44,6 +45,10 @@ def __init__(self, path, subset=None):
         self._items = list(self._load_items(path).values())
 
     def _load_categories(self, path):
+        if has_meta_file(self._dataset_dir):
+            return { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(self._dataset_dir).keys())) }
+
         label_cat = LabelCategories()
         if osp.isfile(path):
             with open(path, encoding='utf-8') as labels_file:
@@ -188,6 +193,10 @@ class LfwConverter(Converter):
     DEFAULT_IMAGE_EXT = LfwPath.IMAGE_EXT
 
     def apply(self):
+        os.makedirs(self._save_dir, exist_ok=True)
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+
         for subset_name, subset in self._extractor.subsets().items():
             label_categories = self._extractor.categories()[AnnotationType.label]
             labels = {label.name: 0 for label in label_categories}
diff --git a/datumaro/plugins/mnist_csv_format.py b/datumaro/plugins/mnist_csv_format.py
index d79525e347..e6ec6c1fb5 100644
--- a/datumaro/plugins/mnist_csv_format.py
+++ b/datumaro/plugins/mnist_csv_format.py
@@ -12,6 +12,7 @@
 )
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class MnistCsvPath:
@@ -35,6 +36,10 @@ def __init__(self, path, subset=None):
         self._items = list(self._load_items(path).values())
 
     def _load_categories(self):
+        if has_meta_file(self._dataset_dir):
+            return { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(self._dataset_dir).keys())) }
+
         label_cat = LabelCategories()
 
         labels_file = osp.join(self._dataset_dir, 'labels.txt')
@@ -100,6 +105,9 @@ class MnistCsvConverter(Converter):
 
     def apply(self):
         os.makedirs(self._save_dir, exist_ok=True)
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+
         for subset_name, subset in self._extractor.subsets().items():
             data = []
             item_ids = {}
diff --git a/datumaro/plugins/mnist_format.py b/datumaro/plugins/mnist_format.py
index fe8ca91f85..4c89857c68 100644
--- a/datumaro/plugins/mnist_format.py
+++ b/datumaro/plugins/mnist_format.py
@@ -13,6 +13,7 @@
 )
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import DatasetItem, Importer, SourceExtractor
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class MnistPath:
@@ -43,6 +44,10 @@ def __init__(self, path, subset=None):
         self._items = list(self._load_items(path).values())
 
     def _load_categories(self):
+        if has_meta_file(self._dataset_dir):
+            return { AnnotationType.label: LabelCategories().
+                from_iterable(list(parse_meta_file(self._dataset_dir).keys())) }
+
         label_cat = LabelCategories()
 
         labels_file = osp.join(self._dataset_dir, 'labels.txt')
@@ -116,6 +121,9 @@ class MnistConverter(Converter):
 
     def apply(self):
         os.makedirs(self._save_dir, exist_ok=True)
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+
         for subset_name, subset in self._extractor.subsets().items():
             labels = []
             images = np.array([])
diff --git a/datumaro/plugins/synthia_format.py b/datumaro/plugins/synthia_format.py
index a318d97103..5754d6bb6e 100644
--- a/datumaro/plugins/synthia_format.py
+++ b/datumaro/plugins/synthia_format.py
@@ -14,6 +14,7 @@
 from datumaro.components.format_detection import FormatDetectionContext
 from datumaro.util.image import find_images, load_image
 from datumaro.util.mask_tools import generate_colormap, lazy_mask
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 
 
 class SynthiaPath:
@@ -98,6 +99,8 @@ def __init__(self, path):
         self._items = list(self._load_items(path).values())
 
     def _load_categories(self, path):
+        if has_meta_file(path):
+            return make_categories(parse_meta_file(path))
         label_map_path = osp.join(path, SynthiaPath.LABELMAP_FILE)
         if osp.isfile(label_map_path):
             label_map = parse_label_map(label_map_path)
diff --git a/datumaro/plugins/yolo_format/converter.py b/datumaro/plugins/yolo_format/converter.py
index 99896fa888..7c7be42777 100644
--- a/datumaro/plugins/yolo_format/converter.py
+++ b/datumaro/plugins/yolo_format/converter.py
@@ -35,6 +35,9 @@ def apply(self):
 
         os.makedirs(save_dir, exist_ok=True)
 
+        if self._save_dataset_meta:
+            self._save_meta_file(self._save_dir)
+
         label_categories = extractor.categories()[AnnotationType.label]
         label_ids = {label.name: idx
             for idx, label in enumerate(label_categories.items)}
diff --git a/datumaro/plugins/yolo_format/extractor.py b/datumaro/plugins/yolo_format/extractor.py
index ac4f9076cc..0fbae06a33 100644
--- a/datumaro/plugins/yolo_format/extractor.py
+++ b/datumaro/plugins/yolo_format/extractor.py
@@ -15,6 +15,7 @@
 from datumaro.util.image import (
     DEFAULT_IMAGE_META_FILE_NAME, load_image_meta_file,
 )
+from datumaro.util.meta_file_util import has_meta_file, parse_meta_file
 from datumaro.util.os_util import split_path
 
 from .format import YoloPath
@@ -171,6 +172,10 @@ def _parse_annotations(anno_path, image):
 
     @staticmethod
     def _load_categories(names_path):
+        if has_meta_file(osp.dirname(names_path)):
+            return LabelCategories().from_iterable(
+                list(parse_meta_file(osp.dirname(names_path)).keys()))
+
         label_categories = LabelCategories()
 
         with open(names_path, 'r', encoding='utf-8') as f:
diff --git a/site/content/en/docs/formats/ade20k2017.md b/site/content/en/docs/formats/ade20k2017.md
index a3c523cd3d..6c3fc6bc52 100644
--- a/site/content/en/docs/formats/ade20k2017.md
+++ b/site/content/en/docs/formats/ade20k2017.md
@@ -43,6 +43,7 @@ ADE20K dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 dataset/
+├── dataset_meta.json # a list of non-format labels (optional)
 ├── subset1/
 │   └── super_label_1/
 │       ├── img1.jpg
@@ -82,6 +83,8 @@ image. Each line in the text file contains:
 Each column is separated by a `#`. See example of dataset
 [here](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/ade20k2017_dataset).
 
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert an ADE20K dataset into any other format [Datumaro supports](/docs/user-manual/supported_formats/).
diff --git a/site/content/en/docs/formats/ade20k2020.md b/site/content/en/docs/formats/ade20k2020.md
index af4b912e24..fe79939291 100644
--- a/site/content/en/docs/formats/ade20k2020.md
+++ b/site/content/en/docs/formats/ade20k2020.md
@@ -43,6 +43,7 @@ ADE20K dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 dataset/
+├── dataset_meta.json # a list of non-format labels (optional)
 ├── subset1/
 │   ├── img1/  # directory with instance masks for img1
 │   |    ├── instance_001_img1.png
@@ -101,6 +102,8 @@ See our [tests asset](https://github.com/openvinotoolkit/datumaro/tree/develop/t
 for example of this file,
 or check [ADE20K toolkit](https://github.com/CSAILVision/ADE20K) for it.
 
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert an ADE20K dataset into any other format [Datumaro supports](/docs/user-manual/supported_formats/).
diff --git a/site/content/en/docs/formats/align_celeba.md b/site/content/en/docs/formats/align_celeba.md
index 59bb79c3b9..dca8ad5ff0 100644
--- a/site/content/en/docs/formats/align_celeba.md
+++ b/site/content/en/docs/formats/align_celeba.md
@@ -48,6 +48,7 @@ Align CelebA dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 dataset/
+├── dataset_meta.json # a list of non-format labels (optional)
 ├── Anno/
 │   ├── identity_CelebA.txt
 │   ├── list_attr_celeba.txt
@@ -69,6 +70,8 @@ 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`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert an align CelebA dataset into any other format [Datumaro supports](/docs/user-manual/supported_formats/).
diff --git a/site/content/en/docs/formats/celeba.md b/site/content/en/docs/formats/celeba.md
index 7bf40fbf9c..e975a1eedd 100644
--- a/site/content/en/docs/formats/celeba.md
+++ b/site/content/en/docs/formats/celeba.md
@@ -48,6 +48,7 @@ CelebA dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 dataset/
+├── dataset_meta.json # a list of non-format labels (optional)
 ├── Anno/
 │   ├── identity_CelebA.txt
 │   ├── list_attr_celeba.txt
@@ -71,6 +72,8 @@ attributes, bounding boxes, landmarks and subsets respectively
 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`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert a CelebA dataset into any other format [Datumaro supports](/docs/user-manual/supported_formats/).
diff --git a/site/content/en/docs/formats/cifar.md b/site/content/en/docs/formats/cifar.md
index 8f69aaf94e..b21177f016 100644
--- a/site/content/en/docs/formats/cifar.md
+++ b/site/content/en/docs/formats/cifar.md
@@ -46,6 +46,7 @@ CIFAR-10 dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ Dataset/
+    ├── dataset_meta.json # a list of non-format labels (optional)
     ├── batches.meta
     ├── <subset_name1>
     ├── <subset_name2>
@@ -57,6 +58,7 @@ CIFAR-100 dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ Dataset/
+    ├── dataset_meta.json # a list of non-format labels (optional)
     ├── meta
     ├── <subset_name1>
     ├── <subset_name2>
@@ -100,6 +102,8 @@ CIFAR-100:
     'coarse_labels': list of integers
 ```
 
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert a CIFAR dataset into any other format [Datumaro supports](/docs/user-manual/supported_formats).
@@ -147,6 +151,8 @@ Extra options for exporting to CIFAR format:
   (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`.
diff --git a/site/content/en/docs/formats/cityscapes.md b/site/content/en/docs/formats/cityscapes.md
index d37f6838cd..e110dac60c 100644
--- a/site/content/en/docs/formats/cityscapes.md
+++ b/site/content/en/docs/formats/cityscapes.md
@@ -35,8 +35,8 @@ Cityscapes dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ Dataset/
-    ├── dataset_meta.json # a list of non-Pascal labels (optional)
-    ├── label_colors.txt # a list of non-Pascal labels in other format (optional)
+    ├── 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, ...>
@@ -68,12 +68,11 @@ Annotated files description:
   is the instance ID. If a certain annotation describes multiple instances,
   then the pixels have the regular ID of that class
 
-To add unformatted classes, you can use `dataset_meta.json` and `label_colors.txt`.
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file)
+and `label_colors.txt`.
 If the `dataset_meta.json` is not represented in the dataset, then
 `label_colors.txt` will be imported if possible.
 
-Information about `dataset_meta.json` can be found [here](/docs/user_manual/supported_formats/#dataset-meta-file).
-
 In `label_colors.txt` you can define custom color map and non-cityscapes labels,
 for example:
 
@@ -130,7 +129,7 @@ Extra options for exporting to Cityscapes format:
   (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-meta-file` - allow to export dataset with saving dataset meta
+- `--save-dataset-meta` - allow to export dataset with saving dataset meta
   file (by default `False`)
 - `--label_map` allow to define a custom colormap. Example:
 
diff --git a/site/content/en/docs/formats/kitti.md b/site/content/en/docs/formats/kitti.md
index 178d48bed2..fde450c3d7 100644
--- a/site/content/en/docs/formats/kitti.md
+++ b/site/content/en/docs/formats/kitti.md
@@ -70,6 +70,7 @@ KITTI segmentation dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ 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/
@@ -99,6 +100,11 @@ KITTI segmentation dataset directory should have the following structure:
             └── ...
 ```
 
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file)
+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:
@@ -156,6 +162,8 @@ Extra options for exporting to KITTI format:
   (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:
diff --git a/site/content/en/docs/formats/mnist.md b/site/content/en/docs/formats/mnist.md
index 1744fea115..26628dfa21 100644
--- a/site/content/en/docs/formats/mnist.md
+++ b/site/content/en/docs/formats/mnist.md
@@ -68,7 +68,8 @@ MNIST dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ Dataset/
-    ├── labels.txt # list of non-digit labels (optional)
+    ├── 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
@@ -80,14 +81,18 @@ MNIST in CSV dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 └─ Dataset/
-    ├── labels.txt # list of non-digit labels (optional)
+    ├── 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
 ```
 
-If the dataset needs non-digit labels, you need to add the `labels.txt`
-to the dataset folder. For example, `labels.txt` for Fashion MNIST the
-following contents:
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file)
+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:
 
 <!--lint disable fenced-code-flag-->
 ```
@@ -150,6 +155,8 @@ Extra options for exporting to MNIST format:
   (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`.
 
diff --git a/site/content/en/docs/formats/pascal_voc.md b/site/content/en/docs/formats/pascal_voc.md
index b279ceb64a..4c3fe2713b 100644
--- a/site/content/en/docs/formats/pascal_voc.md
+++ b/site/content/en/docs/formats/pascal_voc.md
@@ -108,12 +108,11 @@ 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 unformatted classes, you can use `dataset_meta.json` and `label_map.txt`.
+To add custom classes, you can use [`dataset_meta.json`](/docs/user_manual/supported_formats/#dataset-meta-file)
+and `label_map.txt`.
 If the `dataset_meta.json` is not represented in the dataset, then
 `label_map.txt` will be imported if possible.
 
-Information about `dataset_meta.json` can be found [here](/docs/user_manual/supported_formats/#dataset-meta-file).
-
 In `label_map.txt` you can define custom color map and non-pascal labels,
 for example:
 
@@ -202,7 +201,7 @@ Extra options for exporting to Pascal VOC format:
   (by default `False`)
 - `--image-ext IMAGE_EXT` - allow to specify image extension
   for exporting dataset (by default use original or `.jpg` if none)
-- `--save-meta-file` - allow to export dataset with saving dataset meta
+- `--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`)
diff --git a/site/content/en/docs/formats/synthia.md b/site/content/en/docs/formats/synthia.md
index 2b230c5730..6397cd67cf 100644
--- a/site/content/en/docs/formats/synthia.md
+++ b/site/content/en/docs/formats/synthia.md
@@ -40,6 +40,7 @@ SYNTHIA dataset directory should have the following structure:
 <!--lint disable fenced-code-flag-->
 ```
 dataset/
+├── dataset_meta.json # a list of non-format labels (optional)
 ├── GT/
 │   ├── COLOR/
 │   │   ├── Stereo_Left/
@@ -97,6 +98,7 @@ 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`](/docs/user_manual/supported_formats/#dataset-meta-file).
 
 ## Export to other formats
 
diff --git a/site/content/en/docs/formats/yolo.md b/site/content/en/docs/formats/yolo.md
index 3bf44248e1..33e50ea2f3 100644
--- a/site/content/en/docs/formats/yolo.md
+++ b/site/content/en/docs/formats/yolo.md
@@ -35,6 +35,7 @@ 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
@@ -97,6 +98,8 @@ 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`](/docs/user_manual/supported_formats/#dataset-meta-file).
+
 ## Export to other formats
 
 Datumaro can convert YOLO dataset into any other format
diff --git a/tests/assets/ade20k2017_dataset/training/street/1.jpg b/tests/assets/ade20k2017_dataset/dataset/training/street/1.jpg
similarity index 100%
rename from tests/assets/ade20k2017_dataset/training/street/1.jpg
rename to tests/assets/ade20k2017_dataset/dataset/training/street/1.jpg
diff --git a/tests/assets/ade20k2017_dataset/training/street/1_atr.txt b/tests/assets/ade20k2017_dataset/dataset/training/street/1_atr.txt
similarity index 100%
rename from tests/assets/ade20k2017_dataset/training/street/1_atr.txt
rename to tests/assets/ade20k2017_dataset/dataset/training/street/1_atr.txt
diff --git a/tests/assets/ade20k2017_dataset/training/street/1_parts_1.png b/tests/assets/ade20k2017_dataset/dataset/training/street/1_parts_1.png
similarity index 100%
rename from tests/assets/ade20k2017_dataset/training/street/1_parts_1.png
rename to tests/assets/ade20k2017_dataset/dataset/training/street/1_parts_1.png
diff --git a/tests/assets/ade20k2017_dataset/training/street/1_seg.png b/tests/assets/ade20k2017_dataset/dataset/training/street/1_seg.png
similarity index 100%
rename from tests/assets/ade20k2017_dataset/training/street/1_seg.png
rename to tests/assets/ade20k2017_dataset/dataset/training/street/1_seg.png
diff --git a/tests/assets/ade20k2017_dataset/validation/2.jpg b/tests/assets/ade20k2017_dataset/dataset/validation/2.jpg
similarity index 100%
rename from tests/assets/ade20k2017_dataset/validation/2.jpg
rename to tests/assets/ade20k2017_dataset/dataset/validation/2.jpg
diff --git a/tests/assets/ade20k2017_dataset/validation/2_atr.txt b/tests/assets/ade20k2017_dataset/dataset/validation/2_atr.txt
similarity index 100%
rename from tests/assets/ade20k2017_dataset/validation/2_atr.txt
rename to tests/assets/ade20k2017_dataset/dataset/validation/2_atr.txt
diff --git a/tests/assets/ade20k2017_dataset/validation/2_parts_1.png b/tests/assets/ade20k2017_dataset/dataset/validation/2_parts_1.png
similarity index 100%
rename from tests/assets/ade20k2017_dataset/validation/2_parts_1.png
rename to tests/assets/ade20k2017_dataset/dataset/validation/2_parts_1.png
diff --git a/tests/assets/ade20k2017_dataset/validation/2_parts_2.png b/tests/assets/ade20k2017_dataset/dataset/validation/2_parts_2.png
similarity index 100%
rename from tests/assets/ade20k2017_dataset/validation/2_parts_2.png
rename to tests/assets/ade20k2017_dataset/dataset/validation/2_parts_2.png
diff --git a/tests/assets/ade20k2017_dataset/validation/2_seg.png b/tests/assets/ade20k2017_dataset/dataset/validation/2_seg.png
similarity index 100%
rename from tests/assets/ade20k2017_dataset/validation/2_seg.png
rename to tests/assets/ade20k2017_dataset/dataset/validation/2_seg.png
diff --git a/tests/assets/ade20k2017_dataset/dataset_with_meta_file/dataset_meta.json b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/dataset_meta.json
new file mode 100644
index 0000000000..6fbf31a524
--- /dev/null
+++ b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/dataset_meta.json
@@ -0,0 +1,3 @@
+{
+"label_map": {"0": "sky", "1": "person", "2": "license plate", "3": "rim"}
+}
diff --git a/tests/assets/align_celeba_dataset/Img/img_align_celeba/000001.jpg b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1.jpg
similarity index 100%
rename from tests/assets/align_celeba_dataset/Img/img_align_celeba/000001.jpg
rename to tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1.jpg
diff --git a/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_atr.txt b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_atr.txt
new file mode 100644
index 0000000000..4189f2130c
--- /dev/null
+++ b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_atr.txt
@@ -0,0 +1,3 @@
+001 # 0 # 1 # sky # sky # ""
+002 # 0 # 0 # person, individual, someone, somebody, mortal, soul # person # "walkin"
+001 # 1 # 0 # license plate # license plate # ""
diff --git a/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_parts_1.png b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_parts_1.png
new file mode 100644
index 0000000000..7658479fb2
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diff --git a/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_seg.png b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_seg.png
new file mode 100644
index 0000000000..7badc3ae0d
Binary files /dev/null and b/tests/assets/ade20k2017_dataset/dataset_with_meta_file/training/street/1_seg.png differ
diff --git a/tests/assets/ade20k2020_dataset/training/street/1.jpg b/tests/assets/ade20k2020_dataset/dataset/training/street/1.jpg
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1.jpg
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1.jpg
diff --git a/tests/assets/ade20k2020_dataset/training/street/1.json b/tests/assets/ade20k2020_dataset/dataset/training/street/1.json
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1.json
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1.json
diff --git a/tests/assets/ade20k2020_dataset/training/street/1/instance_000_ADE_train_1.png b/tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_000_ADE_train_1.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1/instance_000_ADE_train_1.png
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_000_ADE_train_1.png
diff --git a/tests/assets/ade20k2020_dataset/training/street/1/instance_001_ADE_train_1.png b/tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_001_ADE_train_1.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1/instance_001_ADE_train_1.png
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_001_ADE_train_1.png
diff --git a/tests/assets/ade20k2020_dataset/training/street/1/instance_002_ADE_train_1.png b/tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_002_ADE_train_1.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1/instance_002_ADE_train_1.png
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1/instance_002_ADE_train_1.png
diff --git a/tests/assets/ade20k2020_dataset/training/street/1_parts_1.png b/tests/assets/ade20k2020_dataset/dataset/training/street/1_parts_1.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1_parts_1.png
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1_parts_1.png
diff --git a/tests/assets/ade20k2020_dataset/training/street/1_seg.png b/tests/assets/ade20k2020_dataset/dataset/training/street/1_seg.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/training/street/1_seg.png
rename to tests/assets/ade20k2020_dataset/dataset/training/street/1_seg.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2.jpg b/tests/assets/ade20k2020_dataset/dataset/validation/2.jpg
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2.jpg
rename to tests/assets/ade20k2020_dataset/dataset/validation/2.jpg
diff --git a/tests/assets/ade20k2020_dataset/validation/2.json b/tests/assets/ade20k2020_dataset/dataset/validation/2.json
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2.json
rename to tests/assets/ade20k2020_dataset/dataset/validation/2.json
diff --git a/tests/assets/ade20k2020_dataset/validation/2/instance_000_ADE_val_2.png b/tests/assets/ade20k2020_dataset/dataset/validation/2/instance_000_ADE_val_2.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2/instance_000_ADE_val_2.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2/instance_000_ADE_val_2.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2/instance_001_ADE_val_2.png b/tests/assets/ade20k2020_dataset/dataset/validation/2/instance_001_ADE_val_2.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2/instance_001_ADE_val_2.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2/instance_001_ADE_val_2.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2/instance_002_ADE_val_2.png b/tests/assets/ade20k2020_dataset/dataset/validation/2/instance_002_ADE_val_2.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2/instance_002_ADE_val_2.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2/instance_002_ADE_val_2.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2/instance_003_ADE_val_2.png b/tests/assets/ade20k2020_dataset/dataset/validation/2/instance_003_ADE_val_2.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2/instance_003_ADE_val_2.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2/instance_003_ADE_val_2.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2_parts_1.png b/tests/assets/ade20k2020_dataset/dataset/validation/2_parts_1.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2_parts_1.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2_parts_1.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2_parts_2.png b/tests/assets/ade20k2020_dataset/dataset/validation/2_parts_2.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2_parts_2.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2_parts_2.png
diff --git a/tests/assets/ade20k2020_dataset/validation/2_seg.png b/tests/assets/ade20k2020_dataset/dataset/validation/2_seg.png
similarity index 100%
rename from tests/assets/ade20k2020_dataset/validation/2_seg.png
rename to tests/assets/ade20k2020_dataset/dataset/validation/2_seg.png
diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/dataset_meta.json b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/dataset_meta.json
new file mode 100644
index 0000000000..773ca0b192
--- /dev/null
+++ b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/dataset_meta.json
@@ -0,0 +1,3 @@
+{
+"label_map": {"0": "car", "1": "person", "2": "door", "3": "rim"}
+}
diff --git a/tests/assets/celeba_dataset/Img/img_celeba/000001.jpg b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1.jpg
similarity index 100%
rename from tests/assets/celeba_dataset/Img/img_celeba/000001.jpg
rename to tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1.jpg
diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1.json b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1.json
new file mode 100644
index 0000000000..5531564711
--- /dev/null
+++ b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1.json
@@ -0,0 +1,110 @@
+{
+  "annotation": {
+    "filename": "1.jpg",
+    "folder": "training/street",
+    "imsize": [
+      5,
+      5,
+      3
+    ],
+    "source": {
+      "folder": "",
+      "filename": "",
+      "origin": ""
+    },
+    "scene": [
+      "outdoor",
+      "urban",
+      "street"
+    ],
+    "object": [
+      {
+        "id": 0,
+        "name": "car, auto, automobile, machine, motorcar",
+        "name_ndx": 401,
+        "hypernym": [
+          "car, auto, automobile, machine, motorcar",
+          "motor vehicle, automotive vehicle",
+          "self-propelled vehicle",
+          "wheeled vehicle",
+          "vehicle",
+          "conveyance, transport",
+          "instrumentality, instrumentation",
+          "artifact, artefact",
+          "whole, unit",
+          "object, physical object",
+          "physical entity",
+          "entity"
+        ],
+        "raw_name": "car",
+        "attributes": [],
+        "depth_ordering_rank": 2,
+        "occluded": [],
+        "crop": 0,
+        "parts": {
+          "hasparts": [],
+          "ispartof": [],
+          "part_level": 0
+        },
+        "instance_mask": "1/instance_000_ADE_train_1.png",
+        "polygon": {
+          "x": [],
+          "y": []
+        },
+        "saved_date": "1-Jan-1970 00:00:00"
+      },
+      {
+        "id": 1,
+        "name": "person, individual, someone, somebody, mortal, soul",
+        "name_ndx": 1831,
+        "hypernym": [
+          "person, individual, someone, somebody, mortal, soul",
+          "organism, being",
+          "living thing, animate thing",
+          "whole, unit",
+          "object, physical object",
+          "physical entity",
+          "entity"
+        ],
+        "raw_name": "person",
+        "attributes": ["walkin"],
+        "depth_ordering_rank": 1,
+        "occluded": "no",
+        "crop": 0,
+        "parts": {
+          "hasparts": [],
+          "ispartof": [],
+          "part_level": 0
+        },
+        "instance_mask": "1/instance_001_ADE_train_1.png",
+        "polygon": {
+          "x": [1, 1, 1, 1, 1],
+          "y": [0, 1, 2, 3, 4]
+        },
+        "saved_date": "1-Jan-1970 00:00:00"
+      },
+      {
+        "id": 2,
+        "name": "door",
+        "name_ndx": 774,
+        "hypernym": ["door", "doors", "garage door"],
+        "raw_name": "door",
+        "attributes": [],
+        "depth_ordering_rank": 3,
+        "occluded": "yes",
+        "crop": 0,
+        "parts": {
+          "hasparts": [],
+          "ispartof": 0,
+          "part_level": 1
+        },
+        "instance_mask": "1/instance_002_ADE_train_1.png",
+        "polygon": {
+          "x": [],
+          "y": []
+        },
+        "saved_date": "1-Jan-1970 00:00:00"
+      }
+        ]
+  }
+}
diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1/instance_000_ADE_train_1.png b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1/instance_000_ADE_train_1.png
new file mode 100644
index 0000000000..00c12479f0
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diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1/instance_002_ADE_train_1.png b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1/instance_002_ADE_train_1.png
new file mode 100644
index 0000000000..1a54e43a1b
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diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1_parts_1.png b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1_parts_1.png
new file mode 100644
index 0000000000..dd50d05d87
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diff --git a/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1_seg.png b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1_seg.png
new file mode 100644
index 0000000000..abbc546cd2
Binary files /dev/null and b/tests/assets/ade20k2020_dataset/dataset_with_meta_file/training/street/1_seg.png differ
diff --git a/tests/assets/align_celeba_dataset/Anno/identity_CelebA.txt b/tests/assets/align_celeba_dataset/dataset/Anno/identity_CelebA.txt
similarity index 100%
rename from tests/assets/align_celeba_dataset/Anno/identity_CelebA.txt
rename to tests/assets/align_celeba_dataset/dataset/Anno/identity_CelebA.txt
diff --git a/tests/assets/align_celeba_dataset/Anno/list_attr_celeba.txt b/tests/assets/align_celeba_dataset/dataset/Anno/list_attr_celeba.txt
similarity index 100%
rename from tests/assets/align_celeba_dataset/Anno/list_attr_celeba.txt
rename to tests/assets/align_celeba_dataset/dataset/Anno/list_attr_celeba.txt
diff --git a/tests/assets/align_celeba_dataset/Anno/list_landmarks_align_celeba.txt b/tests/assets/align_celeba_dataset/dataset/Anno/list_landmarks_align_celeba.txt
similarity index 100%
rename from tests/assets/align_celeba_dataset/Anno/list_landmarks_align_celeba.txt
rename to tests/assets/align_celeba_dataset/dataset/Anno/list_landmarks_align_celeba.txt
diff --git a/tests/assets/align_celeba_dataset/Eval/list_eval_partition.txt b/tests/assets/align_celeba_dataset/dataset/Eval/list_eval_partition.txt
similarity index 100%
rename from tests/assets/align_celeba_dataset/Eval/list_eval_partition.txt
rename to tests/assets/align_celeba_dataset/dataset/Eval/list_eval_partition.txt
diff --git a/tests/assets/align_celeba_dataset/Img/img_align_celeba/000002.jpg b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000001.jpg
similarity index 100%
rename from tests/assets/align_celeba_dataset/Img/img_align_celeba/000002.jpg
rename to tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000001.jpg
diff --git a/tests/assets/align_celeba_dataset/Img/img_align_celeba/000003.jpg b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000002.jpg
similarity index 100%
rename from tests/assets/align_celeba_dataset/Img/img_align_celeba/000003.jpg
rename to tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000002.jpg
diff --git a/tests/assets/align_celeba_dataset/Img/img_align_celeba/000004.jpg b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000003.jpg
similarity index 100%
rename from tests/assets/align_celeba_dataset/Img/img_align_celeba/000004.jpg
rename to tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000003.jpg
diff --git a/tests/assets/align_celeba_dataset/Img/img_align_celeba/000005.jpg b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000004.jpg
similarity index 100%
rename from tests/assets/align_celeba_dataset/Img/img_align_celeba/000005.jpg
rename to tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000004.jpg
diff --git a/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000005.jpg b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000005.jpg
new file mode 100644
index 0000000000..3bd4d24c49
Binary files /dev/null and b/tests/assets/align_celeba_dataset/dataset/Img/img_align_celeba/000005.jpg differ
diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt
new file mode 100644
index 0000000000..1e4441ce56
--- /dev/null
+++ b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt
@@ -0,0 +1,5 @@
+000001.jpg 1
+000002.jpg 3
+000003.jpg 0
+000004.jpg 2
+000005.jpg 6
diff --git a/tests/assets/celeba_dataset/Eval/list_eval_partition.txt b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Eval/list_eval_partition.txt
similarity index 100%
rename from tests/assets/celeba_dataset/Eval/list_eval_partition.txt
rename to tests/assets/align_celeba_dataset/dataset_with_meta_file/Eval/list_eval_partition.txt
diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000001.jpg b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000001.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000002.jpg b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000002.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000003.jpg b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000003.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000004.jpg b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000004.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000005.jpg b/tests/assets/align_celeba_dataset/dataset_with_meta_file/Img/img_align_celeba/000005.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/align_celeba_dataset/dataset_with_meta_file/dataset_meta.json b/tests/assets/align_celeba_dataset/dataset_with_meta_file/dataset_meta.json
new file mode 100644
index 0000000000..b63f44812e
--- /dev/null
+++ b/tests/assets/align_celeba_dataset/dataset_with_meta_file/dataset_meta.json
@@ -0,0 +1,3 @@
+{
+"labels": ["class-0", "class-1", "class-2", "class-3", "class-4", "class-5", "class-6"]
+}
diff --git a/tests/assets/celeba_dataset/Anno/identity_CelebA.txt b/tests/assets/celeba_dataset/dataset/Anno/identity_CelebA.txt
similarity index 100%
rename from tests/assets/celeba_dataset/Anno/identity_CelebA.txt
rename to tests/assets/celeba_dataset/dataset/Anno/identity_CelebA.txt
diff --git a/tests/assets/celeba_dataset/Anno/list_attr_celeba.txt b/tests/assets/celeba_dataset/dataset/Anno/list_attr_celeba.txt
similarity index 100%
rename from tests/assets/celeba_dataset/Anno/list_attr_celeba.txt
rename to tests/assets/celeba_dataset/dataset/Anno/list_attr_celeba.txt
diff --git a/tests/assets/celeba_dataset/Anno/list_bbox_celeba.txt b/tests/assets/celeba_dataset/dataset/Anno/list_bbox_celeba.txt
similarity index 100%
rename from tests/assets/celeba_dataset/Anno/list_bbox_celeba.txt
rename to tests/assets/celeba_dataset/dataset/Anno/list_bbox_celeba.txt
diff --git a/tests/assets/celeba_dataset/Anno/list_landmarks_celeba.txt b/tests/assets/celeba_dataset/dataset/Anno/list_landmarks_celeba.txt
similarity index 100%
rename from tests/assets/celeba_dataset/Anno/list_landmarks_celeba.txt
rename to tests/assets/celeba_dataset/dataset/Anno/list_landmarks_celeba.txt
diff --git a/tests/assets/celeba_dataset/dataset/Eval/list_eval_partition.txt b/tests/assets/celeba_dataset/dataset/Eval/list_eval_partition.txt
new file mode 100644
index 0000000000..620bc5cf82
--- /dev/null
+++ b/tests/assets/celeba_dataset/dataset/Eval/list_eval_partition.txt
@@ -0,0 +1,5 @@
+000001.jpg 0
+000002.jpg 0
+000003.jpg 1
+000004.jpg 2
+000005.jpg 2
diff --git a/tests/assets/celeba_dataset/Img/img_celeba/000002.jpg b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000001.jpg
similarity index 100%
rename from tests/assets/celeba_dataset/Img/img_celeba/000002.jpg
rename to tests/assets/celeba_dataset/dataset/Img/img_celeba/000001.jpg
diff --git a/tests/assets/celeba_dataset/Img/img_celeba/000003.jpg b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000002.jpg
similarity index 100%
rename from tests/assets/celeba_dataset/Img/img_celeba/000003.jpg
rename to tests/assets/celeba_dataset/dataset/Img/img_celeba/000002.jpg
diff --git a/tests/assets/celeba_dataset/Img/img_celeba/000004.jpg b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000003.jpg
similarity index 100%
rename from tests/assets/celeba_dataset/Img/img_celeba/000004.jpg
rename to tests/assets/celeba_dataset/dataset/Img/img_celeba/000003.jpg
diff --git a/tests/assets/celeba_dataset/Img/img_celeba/000005.jpg b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000004.jpg
similarity index 100%
rename from tests/assets/celeba_dataset/Img/img_celeba/000005.jpg
rename to tests/assets/celeba_dataset/dataset/Img/img_celeba/000004.jpg
diff --git a/tests/assets/celeba_dataset/dataset/Img/img_celeba/000005.jpg b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000005.jpg
new file mode 100644
index 0000000000..da0b2003e6
Binary files /dev/null and b/tests/assets/celeba_dataset/dataset/Img/img_celeba/000005.jpg differ
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt b/tests/assets/celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt
new file mode 100644
index 0000000000..1e4441ce56
--- /dev/null
+++ b/tests/assets/celeba_dataset/dataset_with_meta_file/Anno/identity_CelebA.txt
@@ -0,0 +1,5 @@
+000001.jpg 1
+000002.jpg 3
+000003.jpg 0
+000004.jpg 2
+000005.jpg 6
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Eval/list_eval_partition.txt b/tests/assets/celeba_dataset/dataset_with_meta_file/Eval/list_eval_partition.txt
new file mode 100644
index 0000000000..620bc5cf82
--- /dev/null
+++ b/tests/assets/celeba_dataset/dataset_with_meta_file/Eval/list_eval_partition.txt
@@ -0,0 +1,5 @@
+000001.jpg 0
+000002.jpg 0
+000003.jpg 1
+000004.jpg 2
+000005.jpg 2
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000001.jpg b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000001.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000002.jpg b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000002.jpg
new file mode 100644
index 0000000000..3bd4d24c49
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diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000003.jpg b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000003.jpg
new file mode 100644
index 0000000000..3bd4d24c49
Binary files /dev/null and b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000003.jpg differ
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000004.jpg b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000004.jpg
new file mode 100644
index 0000000000..3bd4d24c49
Binary files /dev/null and b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000004.jpg differ
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000005.jpg b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000005.jpg
new file mode 100644
index 0000000000..3bd4d24c49
Binary files /dev/null and b/tests/assets/celeba_dataset/dataset_with_meta_file/Img/img_celeba/000005.jpg differ
diff --git a/tests/assets/celeba_dataset/dataset_with_meta_file/dataset_meta.json b/tests/assets/celeba_dataset/dataset_with_meta_file/dataset_meta.json
new file mode 100644
index 0000000000..b63f44812e
--- /dev/null
+++ b/tests/assets/celeba_dataset/dataset_with_meta_file/dataset_meta.json
@@ -0,0 +1,3 @@
+{
+"labels": ["class-0", "class-1", "class-2", "class-3", "class-4", "class-5", "class-6"]
+}
diff --git a/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000000.png b/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000000.png
new file mode 100644
index 0000000000..04c48c8602
Binary files /dev/null and b/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000000.png differ
diff --git a/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000001.png b/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000001.png
new file mode 100644
index 0000000000..425cf55488
Binary files /dev/null and b/tests/assets/synthia_dataset/dataset_with_meta_file/GT/COLOR/Stereo_Left/Omni_F/000001.png differ
diff --git a/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000000.png b/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000000.png
new file mode 100644
index 0000000000..528f105467
Binary files /dev/null and b/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000000.png differ
diff --git a/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000001.png b/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000001.png
new file mode 100644
index 0000000000..528f105467
Binary files /dev/null and b/tests/assets/synthia_dataset/dataset_with_meta_file/RGB/Stereo_Left/Omni_F/000001.png differ
diff --git a/tests/assets/synthia_dataset/dataset_with_meta_file/dataset_meta.json b/tests/assets/synthia_dataset/dataset_with_meta_file/dataset_meta.json
new file mode 100644
index 0000000000..2357ef2623
--- /dev/null
+++ b/tests/assets/synthia_dataset/dataset_with_meta_file/dataset_meta.json
@@ -0,0 +1,5 @@
+{
+"label_map": {"0": "background", "1": "sky", "2": "building", "3": "person", "4": "road"},
+"background_label": "0",
+"segmentation_colors": [[0, 0, 0], [0, 0, 64], [0, 128, 128], [128, 0, 64], [0, 192, 128]]
+}
diff --git a/tests/cli/test_revpath.py b/tests/cli/test_revpath.py
index 681154eafc..f32a1f39fb 100644
--- a/tests/cli/test_revpath.py
+++ b/tests/cli/test_revpath.py
@@ -138,8 +138,8 @@ def test_ambiguous_format(self):
         assets_dir = osp.join(osp.dirname(__file__), '../assets')
         os.makedirs(annotation_dir)
         for asset in [
-            'ade20k2017_dataset/training/street/1_atr.txt',
-            'ade20k2020_dataset/training/street/1.json',
+            'ade20k2017_dataset/dataset/training/street/1_atr.txt',
+            'ade20k2020_dataset/dataset/training/street/1.json',
         ]:
             shutil.copy(osp.join(assets_dir, asset), annotation_dir)
 
diff --git a/tests/test_ade20k2017_format.py b/tests/test_ade20k2017_format.py
index 3b057be516..5d41f375d7 100644
--- a/tests/test_ade20k2017_format.py
+++ b/tests/test_ade20k2017_format.py
@@ -15,7 +15,11 @@
 
 from tests.requirements import Requirements, mark_requirement
 
-DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'ade20k2017_dataset')
+DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'ade20k2017_dataset',
+    'dataset')
+
+DUMMY_DATASET_DIR_META_FILE = osp.join(osp.dirname(__file__), 'assets', 'ade20k2017_dataset',
+    'dataset_with_meta_file')
 
 class Ade20k2017ImporterTest(TestCase):
     @mark_requirement(Requirements.DATUM_399)
@@ -58,3 +62,27 @@ def test_can_import(self):
         imported_dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'ade20k2017')
         compare_datasets(self, expected_dataset, imported_dataset,
             require_images=True)
+
+    @mark_requirement(Requirements.DATUM_399)
+    def test_can_import_with_meta_file(self):
+        expected_dataset = Dataset.from_iterable(
+            [
+                DatasetItem(id='street/1', subset='training',
+                    image=np.ones((3, 4, 3)),
+                    annotations=[
+                        Mask(image=np.array([[0, 1, 0, 0]] * 3), label=0,
+                            group=1, z_order=0, id=1),
+                        Mask(image=np.array([[0, 0, 0, 1]] * 3), label=2,
+                            group=1, z_order=1, id=1),
+                        Mask(image=np.array([[0, 0, 1, 1]] * 3),
+                            group=2, label=1, z_order=0, id=2,
+                            attributes={'walkin': True})
+                    ])
+            ], categories={AnnotationType.label: LabelCategories.from_iterable([
+                    'sky', 'person', 'license plate', 'rim'])
+                }
+        )
+
+        imported_dataset = Dataset.import_from(DUMMY_DATASET_DIR_META_FILE, 'ade20k2017')
+        compare_datasets(self, expected_dataset, imported_dataset,
+            require_images=True)
diff --git a/tests/test_ade20k2020_format.py b/tests/test_ade20k2020_format.py
index 894a1355bc..d0412eabd2 100644
--- a/tests/test_ade20k2020_format.py
+++ b/tests/test_ade20k2020_format.py
@@ -17,7 +17,11 @@
 
 from tests.requirements import Requirements, mark_requirement
 
-DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'ade20k2020_dataset')
+DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'ade20k2020_dataset',
+    'dataset')
+
+DUMMY_DATASET_DIR_META_FILE = osp.join(osp.dirname(__file__), 'assets', 'ade20k2020_dataset',
+    'dataset_with_meta_file')
 
 class Ade20k2020ImporterTest(TestCase):
     @mark_requirement(Requirements.DATUM_399)
@@ -78,3 +82,36 @@ def test_can_import(self):
         imported_dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'ade20k2020')
         compare_datasets(self, expected_dataset, imported_dataset,
             require_images=True)
+
+    @mark_requirement(Requirements.DATUM_399)
+    def test_can_import_with_meta_file(self):
+        expected_dataset = Dataset.from_iterable(
+            [
+                DatasetItem(id='street/1', subset='training',
+                    image=np.ones((5, 5, 3)),
+                    annotations=[
+                        Polygon([1, 0, 1, 1, 1, 2, 1, 3, 1, 4],
+                            group=1, z_order=0, id=1, label=1,
+                            attributes={'walkin': True}),
+                        Mask(image=np.array([[0, 0, 1, 1, 1]] * 5), label=0,
+                            group=401, z_order=0, id=401),
+                        Mask(image=np.array([[0, 1, 0, 0, 0]] * 5), label=1,
+                            group=1831, z_order=0, id=1831),
+                        Mask(image=np.array([[0, 0, 0, 1, 1]] * 5), label=2,
+                            id=774, group=774, z_order=1),
+                        Mask(image=np.array([[0, 0, 1, 1, 1]] * 5), label=0,
+                            group=0, z_order=0, id=0),
+                        Mask(image=np.array([[0, 1, 0, 0, 0]] * 5), label=1,
+                            group=1, z_order=0, id=1,
+                            attributes={'walkin': True}),
+                        Mask(image=np.array([[0, 0, 0, 1, 1]] * 5), label=2,
+                            group=2, z_order=1, id=2),
+                    ])
+            ], categories={AnnotationType.label: LabelCategories.from_iterable([
+                    'car', 'person', 'door', 'rim'])
+                }
+        )
+
+        imported_dataset = Dataset.import_from(DUMMY_DATASET_DIR_META_FILE, 'ade20k2020')
+        compare_datasets(self, expected_dataset, imported_dataset,
+            require_images=True)
diff --git a/tests/test_align_celeba_format.py b/tests/test_align_celeba_format.py
index 9eefcc0daa..a12f143832 100644
--- a/tests/test_align_celeba_format.py
+++ b/tests/test_align_celeba_format.py
@@ -15,9 +15,12 @@
 from .requirements import Requirements, mark_requirement
 
 DUMMY_ALIGN_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets',
-    'align_celeba_dataset',)
+    'align_celeba_dataset', 'dataset')
+DUMMY_ALIGN_DATASET_DIR_WITH_META_FILE = osp.join(osp.dirname(__file__),
+    'assets', 'align_celeba_dataset', 'dataset_with_meta_file')
 
 class AlignCelebaImporterTest(TestCase):
+    @mark_requirement(Requirements.DATUM_475)
     def test_can_import(self):
         expected_dataset = Dataset.from_iterable([
             DatasetItem(id='000001', subset='train',
@@ -79,6 +82,36 @@ def test_can_import(self):
 
         compare_datasets(self, expected_dataset, dataset, require_images=True)
 
+    @mark_requirement(Requirements.DATUM_475)
+    def test_can_import_with_meta_file(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='000001', subset='train',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(1)]
+            ),
+            DatasetItem(id='000002', subset='train',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(3)]
+            ),
+            DatasetItem(id='000003', subset='val',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id='000004', subset='test',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(2)]
+            ),
+            DatasetItem(id='000005', subset='test',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(6)]
+            )
+        ], categories=[f'class-{i}' for i in range(7)])
+
+        dataset = Dataset.import_from(DUMMY_ALIGN_DATASET_DIR_WITH_META_FILE,
+            'align_celeba')
+
+        compare_datasets(self, expected_dataset, dataset, require_images=True)
+
     @mark_requirement(Requirements.DATUM_475)
     def test_can_detect_align_dataset(self):
         detected_formats = Environment().detect_dataset(DUMMY_ALIGN_DATASET_DIR)
diff --git a/tests/test_celeba_format.py b/tests/test_celeba_format.py
index a9985fd93b..d793900718 100644
--- a/tests/test_celeba_format.py
+++ b/tests/test_celeba_format.py
@@ -15,7 +15,9 @@
 from .requirements import Requirements, mark_requirement
 
 DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets',
-    'celeba_dataset')
+    'celeba_dataset', 'dataset')
+DUMMY_DATASET_DIR_WITH_META_FILE = osp.join(osp.dirname(__file__),
+    'assets', 'celeba_dataset', 'dataset_with_meta_file')
 
 class CelebaImporterTest(TestCase):
     @mark_requirement(Requirements.DATUM_475)
@@ -85,6 +87,36 @@ def test_can_import(self):
 
         compare_datasets(self, expected_dataset, dataset, require_images=True)
 
+    @mark_requirement(Requirements.DATUM_475)
+    def test_can_import_with_meta_file(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='000001', subset='train',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(1)]
+            ),
+            DatasetItem(id='000002', subset='train',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(3)]
+            ),
+            DatasetItem(id='000003', subset='val',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id='000004', subset='test',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(2)]
+            ),
+            DatasetItem(id='000005', subset='test',
+                image=np.ones((3, 4, 3)),
+                annotations=[Label(6)]
+            )
+        ], categories=[f'class-{i}' for i in range(7)])
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR_WITH_META_FILE,
+            'celeba')
+
+        compare_datasets(self, expected_dataset, dataset, require_images=True)
+
     @mark_requirement(Requirements.DATUM_475)
     def test_can_detect(self):
         detected_formats = Environment().detect_dataset(DUMMY_DATASET_DIR)
diff --git a/tests/test_cifar_format.py b/tests/test_cifar_format.py
index 1f0d414473..6140a9c33e 100644
--- a/tests/test_cifar_format.py
+++ b/tests/test_cifar_format.py
@@ -216,6 +216,30 @@ def test_can_catch_pickle_exception(self):
             with self.assertRaisesRegex(pickle.UnpicklingError, "Global"):
                 Dataset.import_from(test_dir, 'cifar')
 
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_and_load_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id='image_2', subset='test',
+                image=np.ones((32, 32, 3)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id='image_3', subset='test',
+                image=np.ones((32, 32, 3))
+            ),
+            DatasetItem(id='image_4', subset='test',
+                image=np.ones((32, 32, 3)),
+                annotations=[Label(1)]
+            )
+        ], categories=['label_0', 'label_1'])
+
+        with TestDir() as test_dir:
+            CifarConverter.convert(source_dataset, test_dir, save_images=True,
+                save_dataset_meta=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'cifar')
+
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
 
 DUMMY_10_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets',
     'cifar10_dataset')
diff --git a/tests/test_kitti_format.py b/tests/test_kitti_format.py
index 68e18085d6..460d588667 100644
--- a/tests/test_kitti_format.py
+++ b/tests/test_kitti_format.py
@@ -20,6 +20,7 @@
 from datumaro.plugins.kitti_format.importer import (
     KittiDetectionImporter, KittiImporter, KittiSegmentationImporter,
 )
+from datumaro.util.meta_file_util import parse_meta_file
 from datumaro.util.test_utils import (
     TestDir, check_save_and_load, compare_datasets,
 )
@@ -43,6 +44,20 @@ def test_can_write_and_parse_labelmap(self):
 
             self.assertEqual(src_label_map, dst_label_map)
 
+    @mark_requirement(Requirements.DATUM_280)
+    def test_can_write_and_parse_dataset_meta_file(self):
+        src_label_map = KittiLabelMap
+
+        with TestDir() as test_dir:
+            source_dataset = Dataset.from_iterable([],
+                categories=make_kitti_categories(src_label_map))
+
+            KittiConverter.convert(source_dataset, test_dir,
+                save_dataset_meta=True)
+            dst_label_map = parse_meta_file(test_dir)
+
+            self.assertEqual(src_label_map, dst_label_map)
+
 class KittiImportTest(TestCase):
     @mark_requirement(Requirements.DATUM_280)
     def test_can_import_segmentation(self):
@@ -546,3 +561,72 @@ def test_can_save_detection_with_score_attribute(self):
             self._test_save_and_load(source_dataset,
                 partial(KittiConverter.convert,
                 save_images=True, tasks=KittiTask.detection), test_dir)
+
+    @mark_requirement(Requirements.DATUM_280)
+    def test_can_save_detection_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id='1_2', subset='test',
+                image=np.ones((10, 10, 3)), annotations=[
+                Bbox(0, 1, 2, 2, label=0, id=0,
+                    attributes={'truncated': False, 'occluded': False,
+                        'score': 1.0}),
+            ]),
+            DatasetItem(id='1_3', subset='test',
+                image=np.ones((10, 10, 3)), annotations=[
+                Bbox(0, 0, 2, 2, label=1, id=0,
+                    attributes={'truncated': True, 'occluded': False,
+                        'score': 1.0}),
+                Bbox(6, 2, 3, 4, label=1, id=1,
+                    attributes={'truncated': False, 'occluded': True,
+                        'score': 1.0}),
+            ]),
+        ], categories=['label_0', 'label_1'])
+
+        with TestDir() as test_dir:
+            self._test_save_and_load(source_dataset,
+                partial(KittiConverter.convert,
+                save_images=True, save_dataset_meta=True,
+                tasks=KittiTask.detection), test_dir)
+
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_segmentation_with_meta_file(self):
+        class SrcExtractor(TestExtractorBase):
+            def __iter__(self):
+                yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
+                    Mask(image=np.array([[1, 0, 0, 1, 1]]), label=1, id=1,
+                        attributes={'is_crowd': False}),
+                    Mask(image=np.array([[0, 1, 1, 0, 0]]), label=2, id=2,
+                        attributes={'is_crowd': False}),
+                ])
+
+            def categories(self):
+                label_map = OrderedDict()
+                label_map['background'] = (0, 0, 0)
+                label_map['label_1'] = (1, 2, 3)
+                label_map['label_2'] = (3, 2, 1)
+                return make_kitti_categories(label_map)
+
+        class DstExtractor(TestExtractorBase):
+            def __iter__(self):
+                yield DatasetItem(id=1, image=np.ones((1, 5, 3)), annotations=[
+                    Mask(image=np.array([[1, 0, 0, 1, 1]]),
+                        attributes={'is_crowd': False}, id=1,
+                        label=self._label('label_1')),
+                    Mask(image=np.array([[0, 1, 1, 0, 0]]),
+                        attributes={'is_crowd': False}, id=2,
+                        label=self._label('label_2')),
+                ])
+
+            def categories(self):
+                label_map = OrderedDict()
+                label_map['background'] = (0, 0, 0)
+                label_map['label_1'] = (1, 2, 3)
+                label_map['label_2'] = (3, 2, 1)
+                return make_kitti_categories(label_map)
+
+        with TestDir() as test_dir:
+            self._test_save_and_load(SrcExtractor(),
+                partial(KittiConverter.convert, label_map='source',
+                save_images=True, save_dataset_meta=True), test_dir,
+                target_dataset=DstExtractor())
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
diff --git a/tests/test_lfw_format.py b/tests/test_lfw_format.py
index 1173315d1f..0182ec84c3 100644
--- a/tests/test_lfw_format.py
+++ b/tests/test_lfw_format.py
@@ -201,6 +201,46 @@ def test_can_save_and_load_image_with_arbitrary_extension(self):
 
             compare_datasets(self, dataset, parsed_dataset, require_images=True)
 
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_and_load_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id='name0_0001', subset='test',
+                image=np.ones((2, 5, 3)),
+                annotations=[Label(0, attributes={
+                    'positive_pairs': ['name0/name0_0002']
+                })]
+            ),
+            DatasetItem(id='name0_0002', subset='test',
+                image=np.ones((2, 5, 3)),
+                annotations=[Label(0, attributes={
+                    'positive_pairs': ['name0/name0_0001'],
+                    'negative_pairs': ['name1/name1_0001']
+                })]
+            ),
+            DatasetItem(id='name1_0001', subset='test',
+                image=np.ones((2, 5, 3)),
+                annotations=[Label(1, attributes={
+                    'positive_pairs': ['name1/name1_0002']
+                })]
+            ),
+            DatasetItem(id='name1_0002', subset='test',
+                image=np.ones((2, 5, 3)),
+                annotations=[Label(1, attributes={
+                    'positive_pairs': ['name1/name1_0002'],
+                    'negative_pairs': ['name0/name0_0001']
+                })]
+            ),
+        ], categories=['name0', 'name1'])
+
+        with TestDir() as test_dir:
+            LfwConverter.convert(source_dataset, test_dir,
+                save_images=True, save_dataset_meta=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'lfw')
+
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
 DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'lfw_dataset')
 
 class LfwImporterTest(TestCase):
diff --git a/tests/test_mnist_csv_format.py b/tests/test_mnist_csv_format.py
index a2e97cb192..f660763db8 100644
--- a/tests/test_mnist_csv_format.py
+++ b/tests/test_mnist_csv_format.py
@@ -161,6 +161,34 @@ def test_can_save_and_load_with_other_labels(self):
             compare_datasets(self, dataset, parsed_dataset,
                 require_images=True)
 
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_and_load_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, subset='test',
+                image=np.ones((28, 28))
+            ),
+            DatasetItem(id=2, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(1)]
+            )
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistCsvConverter.convert(source_dataset, test_dir, save_images=True,
+                save_dataset_meta=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
 DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mnist_csv_dataset')
 
 class MnistCsvImporterTest(TestCase):
diff --git a/tests/test_mnist_format.py b/tests/test_mnist_format.py
index c576e8e351..61860a1d07 100644
--- a/tests/test_mnist_format.py
+++ b/tests/test_mnist_format.py
@@ -159,6 +159,34 @@ def test_can_save_and_load_with_other_labels(self):
             compare_datasets(self, dataset, parsed_dataset,
                 require_images=True)
 
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_and_load_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, subset='test',
+                image=np.ones((28, 28))
+            ),
+            DatasetItem(id=2, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(1)]
+            )
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistConverter.convert(source_dataset, test_dir, save_images=True,
+                save_dataset_meta=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
 DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mnist_dataset')
 
 class MnistImporterTest(TestCase):
diff --git a/tests/test_synthia_format.py b/tests/test_synthia_format.py
index b092ba6bba..f28396c847 100644
--- a/tests/test_synthia_format.py
+++ b/tests/test_synthia_format.py
@@ -24,6 +24,9 @@
 DUMMY_DATASET_DIR_CUSTOM_LABELMAP = osp.join(osp.dirname(__file__),
     'assets', 'synthia_dataset', 'dataset_with_custom_labelmap')
 
+DUMMY_DATASET_DIR_META_FILE = osp.join(osp.dirname(__file__),
+    'assets', 'synthia_dataset', 'dataset_with_meta_file')
+
 class SynthiaImporterTest(TestCase):
     @mark_requirement(Requirements.DATUM_497)
     def test_can_detect(self):
@@ -148,3 +151,32 @@ def test_can_import_with_custom_labelmap(self):
         dataset = Dataset.import_from(DUMMY_DATASET_DIR_CUSTOM_LABELMAP, 'synthia')
 
         compare_datasets(self, expected_dataset, dataset, require_images=True)
+
+    @mark_requirement(Requirements.DATUM_497)
+    def test_can_import_with_meta_file(self):
+        expected_dataset = Dataset.from_iterable([
+            DatasetItem(id='Stereo_Left/Omni_F/000000',
+                image=np.ones((1, 5, 3)),
+                annotations=[
+                    Mask(np.array([[1, 1, 1, 0, 0]]), label=1),
+                    Mask(np.array([[0, 0, 0, 1, 1]]), label=4),
+                ],
+            ),
+            DatasetItem(id='Stereo_Left/Omni_F/000001',
+                image=np.ones((1, 5, 3)),
+                annotations=[
+                    Mask(np.array([[1, 1, 0, 0, 0]]), label=2),
+                    Mask(np.array([[0, 0, 1, 1, 0]]), label=3),
+                    Mask(np.array([[0, 0, 0, 0, 1]]), label=4),
+                ],
+            )
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                ['background', 'sky', 'building', 'person', 'road']),
+            AnnotationType.mask: MaskCategories({0: (0, 0, 0), 1: (0, 0, 64),
+                2: (0, 128, 128), 3: (128, 0, 64), 4: (0, 192, 128)})
+        })
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR_META_FILE, 'synthia')
+
+        compare_datasets(self, expected_dataset, dataset, require_images=True)
diff --git a/tests/test_yolo_format.py b/tests/test_yolo_format.py
index f15a0b9f93..7ffd2fd857 100644
--- a/tests/test_yolo_format.py
+++ b/tests/test_yolo_format.py
@@ -179,6 +179,41 @@ def test_inplace_save_writes_only_updated_data(self):
             compare_datasets(self, expected, Dataset.import_from(path, 'yolo'),
                 require_images=True)
 
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_save_and_load_with_meta_file(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=1, subset='train', image=np.ones((8, 8, 3)),
+                annotations=[
+                    Bbox(0, 2, 4, 2, label=2),
+                    Bbox(0, 1, 2, 3, label=4),
+                ]),
+            DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
+                annotations=[
+                    Bbox(0, 2, 4, 2, label=2),
+                    Bbox(3, 3, 2, 3, label=4),
+                    Bbox(2, 1, 2, 3, label=4),
+                ]),
+
+            DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)),
+                annotations=[
+                    Bbox(0, 1, 5, 2, label=2),
+                    Bbox(0, 2, 3, 2, label=5),
+                    Bbox(0, 2, 4, 2, label=6),
+                    Bbox(0, 7, 3, 2, label=7),
+                ]),
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                'label_' + str(i) for i in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            YoloConverter.convert(source_dataset, test_dir, save_images=True,
+                save_dataset_meta=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'yolo')
+
+            self.assertTrue(osp.isfile(osp.join(test_dir, 'dataset_meta.json')))
+            compare_datasets(self, source_dataset, parsed_dataset)
+
 DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'yolo_dataset')
 
 class YoloImporterTest(TestCase):