diff --git a/CHANGELOG.md b/CHANGELOG.md
index dd78dee5bc03..d177cf58a247 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Support for CIFAR-10/100 dataset format (<https://github.com/openvinotoolkit/datumaro/pull/225>)
 - Support COCO panoptic and stuff format (<https://github.com/openvinotoolkit/datumaro/pull/210>)
 - Documentation file and integration tests for Pascal VOC format (<https://github.com/openvinotoolkit/datumaro/pull/228>)
+- Support for MNIST and MNIST in CSV dataset formats (<https://github.com/openvinotoolkit/datumaro/pull/234>)
 
 ### Changed
 - LabelMe format saves dataset items with their relative paths by subsets without changing names (<https://github.com/openvinotoolkit/datumaro/pull/200>)
diff --git a/README.md b/README.md
index 00697b4bbe23..7bd9d6252b32 100644
--- a/README.md
+++ b/README.md
@@ -134,6 +134,8 @@ CVAT annotations                             ---> Publication, statistics etc.
   - [MOTS PNG](https://www.vision.rwth-aachen.de/page/mots)
   - [ImageNet](http://image-net.org/)
   - [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`)
+  - [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`)
+  - [MNIST in CSV](https://pjreddie.com/projects/mnist-in-csv/) (`classification`)
   - [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
   - [CVAT](https://github.com/opencv/cvat/blob/develop/cvat/apps/documentation/xml_format.md)
   - [LabelMe](http://labelme.csail.mit.edu/Release3.0)
diff --git a/datumaro/plugins/mnist_csv_format.py b/datumaro/plugins/mnist_csv_format.py
new file mode 100644
index 000000000000..ae0fa8bf8c80
--- /dev/null
+++ b/datumaro/plugins/mnist_csv_format.py
@@ -0,0 +1,170 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import os
+import os.path as osp
+
+import numpy as np
+from datumaro.components.converter import Converter
+from datumaro.components.extractor import (AnnotationType, DatasetItem,
+    Importer, Label, LabelCategories, SourceExtractor)
+
+
+class MnistCsvPath:
+    IMAGE_SIZE = 28
+    NONE_LABEL = -1
+
+class MnistCsvExtractor(SourceExtractor):
+    def __init__(self, path, subset=None):
+        if not osp.isfile(path):
+            raise FileNotFoundError("Can't read annotation file '%s'" % path)
+
+        if not subset:
+            file_name = osp.splitext(osp.basename(path))[0]
+            subset = file_name.rsplit('_', maxsplit=1)[-1]
+
+        super().__init__(subset=subset)
+        self._dataset_dir = osp.dirname(path)
+
+        self._categories = self._load_categories()
+
+        self._items = list(self._load_items(path).values())
+
+    def _load_categories(self):
+        label_cat = LabelCategories()
+
+        labels_file = osp.join(self._dataset_dir, 'labels.txt')
+        if osp.isfile(labels_file):
+            with open(labels_file, encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    label_cat.add(line)
+        else:
+            for i in range(10):
+                label_cat.add(str(i))
+
+        return { AnnotationType.label: label_cat }
+
+    def _load_items(self, path):
+        items = {}
+        with open(path, 'r', encoding='utf-8') as f:
+            annotation_table = f.readlines()
+
+        metafile = osp.join(self._dataset_dir, 'meta_%s.csv' % self._subset)
+        meta = []
+        if osp.isfile(metafile):
+            with open(metafile, 'r', encoding='utf-8') as f:
+                meta = f.readlines()
+
+        for i, data in enumerate(annotation_table):
+            data = data.split(',')
+            item_anno = []
+            label = int(data[0])
+            if label != MnistCsvPath.NONE_LABEL:
+                item_anno.append(Label(label))
+
+            if 0 < len(meta):
+                meta[i] = meta[i].strip().split(',')
+
+            # support for single-channel image only
+            image = None
+            if 1 < len(data):
+                if 0 < len(meta) and 1 < len(meta[i]):
+                    image = np.array([int(pix) for pix in data[1:]],
+                        dtype='uint8').reshape(int(meta[i][-2]), int(meta[i][-1]))
+                else:
+                    image = np.array([int(pix) for pix in data[1:]],
+                        dtype='uint8').reshape(28, 28)
+
+            if 0 < len(meta) and len(meta[i]) in [1, 3]:
+                i = meta[i][0]
+
+            items[i] = DatasetItem(id=i, subset=self._subset,
+                image=image, annotations=item_anno)
+        return items
+
+class MnistCsvImporter(Importer):
+    @classmethod
+    def find_sources(cls, path):
+        return cls._find_sources_recursive(path, '.csv', 'mnist_csv',
+            file_filter=lambda p: not osp.basename(p).startswith('meta'))
+
+class MnistCsvConverter(Converter):
+    DEFAULT_IMAGE_EXT = '.png'
+
+    def apply(self):
+        os.makedirs(self._save_dir, exist_ok=True)
+        for subset_name, subset in self._extractor.subsets().items():
+            data = []
+            item_ids = {}
+            image_sizes = {}
+            for item in subset:
+                anns = [a.label for a in item.annotations
+                    if a.type == AnnotationType.label]
+                label = MnistCsvPath.NONE_LABEL
+                if anns:
+                    label = anns[0]
+
+                if item.has_image and self._save_images:
+                    image = item.image
+                    if not image.has_data:
+                        data.append([label, None])
+                    else:
+                        if image.data.shape[0] != MnistCsvPath.IMAGE_SIZE or \
+                                image.data.shape[1] != MnistCsvPath.IMAGE_SIZE:
+                            image_sizes[len(data)] = [image.data.shape[0],
+                                image.data.shape[1]]
+                        image = image.data.reshape(-1).astype(np.uint8).tolist()
+                        image.insert(0, label)
+                        data.append(image)
+                else:
+                    data.append([label])
+
+                if item.id != str(len(data) - 1):
+                    item_ids[len(data) - 1] = item.id
+
+            anno_file = osp.join(self._save_dir, 'mnist_%s.csv' % subset_name)
+            self.save_in_csv(anno_file, data)
+
+            # it is't in the original format,
+            # this is for storng other names and sizes of images
+            if len(item_ids) or len(image_sizes):
+                meta = []
+                if len(item_ids) and len(image_sizes):
+                    # other names and sizes of images
+                    size = [MnistCsvPath.IMAGE_SIZE, MnistCsvPath.IMAGE_SIZE]
+                    for i in range(len(data)):
+                        w, h = image_sizes.get(i, size)
+                        meta.append([item_ids.get(i, i), w, h])
+
+                elif len(item_ids):
+                    # other names of images
+                    for i in range(len(data)):
+                        meta.append([item_ids.get(i, i)])
+
+                elif len(image_sizes):
+                    # other sizes of images
+                    size = [MnistCsvPath.IMAGE_SIZE, MnistCsvPath.IMAGE_SIZE]
+                    for i in range(len(data)):
+                        meta.append(image_sizes.get(i, size))
+
+                metafile = osp.join(self._save_dir, 'meta_%s.csv' % subset_name)
+                self.save_in_csv(metafile, meta)
+
+        self.save_labels()
+
+    def save_in_csv(self, path, data):
+        with open(path, 'w', encoding='utf-8') as f:
+            for row in data:
+                f.write(','.join([str(p) for p in row]) + "\n")
+
+    def save_labels(self):
+        labels_file = osp.join(self._save_dir, 'labels.txt')
+        with open(labels_file, 'w', encoding='utf-8') as f:
+            f.writelines(l.name + '\n'
+                for l in self._extractor.categories().get(
+                    AnnotationType.label, LabelCategories())
+            )
diff --git a/datumaro/plugins/mnist_format.py b/datumaro/plugins/mnist_format.py
new file mode 100644
index 000000000000..0cd97b06dfde
--- /dev/null
+++ b/datumaro/plugins/mnist_format.py
@@ -0,0 +1,209 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import gzip
+import os
+import os.path as osp
+
+import numpy as np
+from datumaro.components.converter import Converter
+from datumaro.components.extractor import (AnnotationType, DatasetItem,
+    Importer, Label, LabelCategories, SourceExtractor)
+
+
+class MnistPath:
+    TEST_LABELS_FILE = 't10k-labels-idx1-ubyte.gz'
+    TEST_IMAGES_FILE = 't10k-images-idx3-ubyte.gz'
+    LABELS_FILE = '-labels-idx1-ubyte.gz'
+    IMAGES_FILE = '-images-idx3-ubyte.gz'
+    IMAGE_SIZE = 28
+    NONE_LABEL = 255
+
+class MnistExtractor(SourceExtractor):
+    def __init__(self, path, subset=None):
+        if not osp.isfile(path):
+            raise FileNotFoundError("Can't read annotation file '%s'" % path)
+
+        if not subset:
+            file_name = osp.splitext(osp.basename(path))[0]
+            if file_name.startswith('t10k'):
+                subset = 'test'
+            else:
+                subset = file_name.split('-', maxsplit=1)[0]
+
+        super().__init__(subset=subset)
+        self._dataset_dir = osp.dirname(path)
+
+        self._categories = self._load_categories()
+
+        self._items = list(self._load_items(path).values())
+
+    def _load_categories(self):
+        label_cat = LabelCategories()
+
+        labels_file = osp.join(self._dataset_dir, 'labels.txt')
+        if osp.isfile(labels_file):
+            with open(labels_file, encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    label_cat.add(line)
+        else:
+            for i in range(10):
+                label_cat.add(str(i))
+
+        return { AnnotationType.label: label_cat }
+
+    def _load_items(self, path):
+        items = {}
+        with gzip.open(path, 'rb') as lbpath:
+            labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)
+
+        meta = []
+        metafile = osp.join(self._dataset_dir, self._subset + '-meta.gz')
+        if osp.isfile(metafile):
+            with gzip.open(metafile, 'rb') as f:
+                meta = np.frombuffer(f.read(), dtype='<U32')
+            meta = meta.reshape(len(labels), int(len(meta) / len(labels)))
+
+        # support for single-channel image only
+        images = None
+        images_file = osp.join(self._dataset_dir,
+            osp.basename(path).replace('labels-idx1', 'images-idx3'))
+        if osp.isfile(images_file):
+            with gzip.open(images_file, 'rb') as imgpath:
+                images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16)
+                if len(meta) == 0 or len(meta[0]) < 2:
+                    images = images.reshape(len(labels), MnistPath.IMAGE_SIZE,
+                        MnistPath.IMAGE_SIZE)
+
+        pix_num = 0
+        for i, annotation in enumerate(labels):
+            annotations = []
+            label = annotation
+            if label != MnistPath.NONE_LABEL:
+                annotations.append(Label(label))
+
+            image = None
+            if images is not None:
+                if 0 < len(meta) and 1 < len(meta[i]):
+                    h, w = int(meta[i][-2]), int(meta[i][-1])
+                    image = images[pix_num : pix_num + h * w].reshape(h, w)
+                    pix_num += h * w
+                else:
+                    image = images[i].reshape(MnistPath.IMAGE_SIZE, MnistPath.IMAGE_SIZE)
+
+            if 0 < len(meta) and (len(meta[i]) == 1 or len(meta[i]) == 3):
+                i = meta[i][0]
+
+            items[i] = DatasetItem(id=i, subset=self._subset,
+                image=image, annotations=annotations)
+        return items
+
+class MnistImporter(Importer):
+    @classmethod
+    def find_sources(cls, path):
+        return cls._find_sources_recursive(path, '.gz', 'mnist',
+            file_filter=lambda p: osp.basename(p).split('-')[1] == 'labels')
+
+class MnistConverter(Converter):
+    DEFAULT_IMAGE_EXT = '.png'
+
+    def apply(self):
+        os.makedirs(self._save_dir, exist_ok=True)
+        for subset_name, subset in self._extractor.subsets().items():
+            labels = []
+            images = np.array([])
+            item_ids = {}
+            image_sizes = {}
+            for item in subset:
+                anns = [a.label for a in item.annotations
+                    if a.type == AnnotationType.label]
+                label = 255
+                if anns:
+                    label = anns[0]
+                labels.append(label)
+
+                if item.id != str(len(labels) - 1):
+                    item_ids[len(labels) - 1] = item.id
+
+                if item.has_image and self._save_images:
+                    image = item.image
+                    if not image.has_data:
+                        image_sizes[len(images) - 1] = [0, 0]
+                    else:
+                        image = image.data
+                        if image.shape[0] != MnistPath.IMAGE_SIZE or \
+                                image.shape[1] != MnistPath.IMAGE_SIZE:
+                            image_sizes[len(labels) - 1] = [image.shape[0], image.shape[1]]
+                        images = np.append(images, image.reshape(-1).astype(np.uint8))
+
+            if subset_name == 'test':
+                labels_file = osp.join(self._save_dir,
+                    MnistPath.TEST_LABELS_FILE)
+            else:
+                labels_file = osp.join(self._save_dir,
+                    subset_name + MnistPath.LABELS_FILE)
+            self.save_annotations(labels_file, labels)
+
+            if 0 < len(images):
+                if subset_name == 'test':
+                    images_file = osp.join(self._save_dir,
+                        MnistPath.TEST_IMAGES_FILE)
+                else:
+                    images_file = osp.join(self._save_dir,
+                        subset_name + MnistPath.IMAGES_FILE)
+                self.save_images(images_file, images)
+
+            # it is't in the original format,
+            # this is for storng other names and sizes of images
+            if len(item_ids) or len(image_sizes):
+                meta = []
+                if len(item_ids) and len(image_sizes):
+                    # other names and sizes of images
+                    size = [MnistPath.IMAGE_SIZE, MnistPath.IMAGE_SIZE]
+                    for i in range(len(labels)):
+                        w, h = image_sizes.get(i, size)
+                        meta.append([item_ids.get(i, i), w, h])
+
+                elif len(item_ids):
+                    # other names of images
+                    for i in range(len(labels)):
+                        meta.append([item_ids.get(i, i)])
+
+                elif len(image_sizes):
+                    # other sizes of images
+                    size = [MnistPath.IMAGE_SIZE, MnistPath.IMAGE_SIZE]
+                    for i in range(len(labels)):
+                        meta.append(image_sizes.get(i, size))
+
+                metafile = osp.join(self._save_dir, subset_name + '-meta.gz')
+                with gzip.open(metafile, 'wb') as f:
+                    f.write(np.array(meta, dtype='<U32').tobytes())
+
+        self.save_labels()
+
+    def save_annotations(self, path, data):
+        with gzip.open(path, 'wb') as f:
+            # magic number = 0x0801 (2049, hexadecimal representation)
+            # this is used to verify the file with MNIST mark data
+            f.write(np.array([0x0801, len(data)], dtype='>i4').tobytes())
+            f.write(np.array(data, dtype='uint8').tobytes())
+
+    def save_images(self, path, data):
+        with gzip.open(path, 'wb') as f:
+            # magic number = 0x0803 (2051, hexadecimal representation),
+            # this is used to verify the file with MNIST image data
+            f.write(np.array([0x0803, len(data), MnistPath.IMAGE_SIZE,
+                MnistPath.IMAGE_SIZE], dtype='>i4').tobytes())
+            f.write(np.array(data, dtype='uint8').tobytes())
+
+    def save_labels(self):
+        labels_file = osp.join(self._save_dir, 'labels.txt')
+        with open(labels_file, 'w', encoding='utf-8') as f:
+            f.writelines(l.name + '\n'
+                for l in self._extractor.categories().get(
+                    AnnotationType.label, LabelCategories())
+            )
diff --git a/docs/mnist_user_manual.md b/docs/mnist_user_manual.md
new file mode 100644
index 000000000000..77ad1e50c878
--- /dev/null
+++ b/docs/mnist_user_manual.md
@@ -0,0 +1,176 @@
+# MNIST user manual
+
+## Contents
+
+- [Format specification](#format-specification)
+- [Load MNIST dataset](#load-MNIST-dataset)
+- [Export to other formats](#export-to-other-formats)
+- [Export to MNIST](#export-to-MNIST)
+- [Particular use cases](#particular-use-cases)
+
+## Format specification
+
+MNIST format specification available [here](http://yann.lecun.com/exdb/mnist/).
+Fashion MNIST format specification available [here](https://github.com/zalandoresearch/fashion-mnist).
+MNIST in CSV  format specification available [here](https://pjreddie.com/projects/mnist-in-csv/).
+
+MNIST dataset format supports `Labels` annotations.
+
+##  Load MNIST dataset
+
+The MNIST dataset is available for free download:
+
+- [train-images-idx3-ubyte.gz](https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz): training set images
+- [train-labels-idx1-ubyte.gz](https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz): training set labels
+- [t10k-images-idx3-ubyte.gz](https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz): test set images
+- [t10k-labels-idx1-ubyte.gz](https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz): test set labels
+
+The Fashion MNIST dataset is available for free download:
+
+- [train-images-idx3-ubyte.gz](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz): training set images
+- [train-labels-idx1-ubyte.gz](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz): training set labels
+- [t10k-images-idx3-ubyte.gz](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz): test set images
+- [t10k-labels-idx1-ubyte.gz](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz): test set labels
+
+The MNIST in CSV dataset is available for free download:
+
+- [mnist_train.csv](https://pjreddie.com/media/files/mnist_train.csv)
+- [mnist_test.csv](https://pjreddie.com/media/files/mnist_test.csv)
+
+There are two ways to create Datumaro project and add MNIST dataset to it:
+
+``` bash
+datum import --format mnist --input-path <path/to/dataset>
+# or
+datum create
+datum add path -f mnist <path/to/dataset>
+```
+
+There are two ways to create Datumaro project and add MNIST in CSV dataset to it:
+
+``` bash
+datum import --format mnist_csv --input-path <path/to/dataset>
+# or
+datum create
+datum add path -f mnist_csv <path/to/dataset>
+```
+
+It is possible to specify project name and project directory run
+`datum create --help` for more information.
+
+MNIST dataset directory should have the following structure:
+
+<!--lint disable fenced-code-flag-->
+```
+└─ Dataset/
+    ├── labels.txt # list of non-digit labels (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:
+
+<!--lint disable fenced-code-flag-->
+```
+└─ Dataset/
+    ├── labels.txt # list of non-digit labels (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 labels contains the following:
+<!--lint disable fenced-code-flag-->
+```
+T-shirt/top
+Trouser
+Pullover
+Dress
+Coat
+Sandal
+Shirt
+Sneaker
+Bag
+Ankle boot
+```
+
+MNIST format only supports single channel 28 x 28 images.
+
+##  Export to other formats
+
+Datumaro can convert MNIST dataset into any other format [Datumaro supports](../docs/user_manual.md#supported-formats).
+To get the expected result, the dataset needs to be converted to formats
+that support the classification task (e.g. CIFAR-10/100, ImageNet, PascalVOC, etc.)
+There are few ways to convert MNIST dataset to other dataset format:
+
+``` bash
+datum project import -f mnist -i <path/to/mnist>
+datum export -f imagenet -o <path/to/output/dir>
+# or
+datum convert -if mnist -i <path/to/mnist> -f imagenet -o <path/to/output/dir>
+```
+
+These commands also work for MNIST in CSV if you use `mnist_csv` instead of `mnist`.
+
+##  Export to MNIST
+
+There are few ways to convert dataset to MNIST format:
+
+``` bash
+# export dataset into MNIST format from existing project
+datum export -p <path/to/project> -f mnist -o <path/to/export/dir> \
+    -- --save-images
+# converting to MNIST format from other format
+datum convert -if imagenet -i <path/to/imagenet/dataset> \
+    -f mnist -o <path/to/export/dir> -- --save-images
+```
+
+Extra options for export 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`).
+
+These commands also work for MNIST in CSV if you use `mnist_csv` instead of `mnist`.
+
+##  Particular use cases
+
+Datumaro supports filtering, transformation, merging etc. for all formats
+and for the MNIST format in particular. Follow [user manual](../docs/user_manual.md)
+to get more information about these operations.
+
+There are few examples of using Datumaro operations to solve
+particular problems with MNIST dataset:
+
+###  Example 1. How to create custom MNIST-like dataset
+
+```python
+from datumaro.components.dataset import Dataset
+from datumaro.components.extractor import Label, DatasetItem
+
+dataset = Dataset.from_iterable([
+    DatasetItem(id=0, image=np.ones((28, 28)),
+        annotations=[Label(2)]
+    ),
+    DatasetItem(id=1, image=np.ones((28, 28)),
+        annotations=[Label(7)]
+    )
+], categories=[str(label) for label in range(10)])
+
+dataset.export('./dataset', format='mnist')
+```
+
+###  Example 2. How to filter and convert MNIST dataset to ImageNet
+
+Convert MNIST dataset to ImageNet format, keep only images with `3` class presented:
+
+``` bash
+# Download MNIST dataset:
+# https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
+# https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
+datum convert --input-format mnist --input-path <path/to/mnist> \
+              --output-format imagenet \
+              --filter '/item[annotation/label="3"]'
+```
diff --git a/docs/user_manual.md b/docs/user_manual.md
index 0f1f57b336fb..550f5e88f59b 100644
--- a/docs/user_manual.md
+++ b/docs/user_manual.md
@@ -118,6 +118,14 @@ List of supported formats:
 - CIFAR-10/100 (`classification` (python version))
   - [Format specification](https://www.cs.toronto.edu/~kriz/cifar.html)
   - [Dataset example](../tests/assets/cifar_dataset)
+- MNIST (`classification`)
+  - [Format specification](http://yann.lecun.com/exdb/mnist/)
+  - [Dataset example](../tests/assets/mnist_dataset)
+  - [Format documentation](./mnist_user_manual.md)
+- MNIST in CSV (`classification`)
+  - [Format specification](https://pjreddie.com/projects/mnist-in-csv/)
+  - [Dataset example](../tests/assets/mnist_csv_dataset)
+  - [Format documentation](./mnist_user_manual.md)
 - CamVid (`segmentation`)
   - [Format specification](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
   - [Dataset example](../tests/assets/camvid_dataset)
diff --git a/tests/assets/mnist_csv_dataset/mnist_test.csv b/tests/assets/mnist_csv_dataset/mnist_test.csv
new file mode 100644
index 000000000000..d07be3847ffc
--- /dev/null
+++ b/tests/assets/mnist_csv_dataset/mnist_test.csv
@@ -0,0 +1,3 @@
+0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
diff --git a/tests/assets/mnist_csv_dataset/mnist_train.csv b/tests/assets/mnist_csv_dataset/mnist_train.csv
new file mode 100644
index 000000000000..c93c7fca9342
--- /dev/null
+++ b/tests/assets/mnist_csv_dataset/mnist_train.csv
@@ -0,0 +1,2 @@
+5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+7,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
diff --git a/tests/assets/mnist_dataset/t10k-images-idx3-ubyte.gz b/tests/assets/mnist_dataset/t10k-images-idx3-ubyte.gz
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diff --git a/tests/assets/mnist_dataset/train-images-idx3-ubyte.gz b/tests/assets/mnist_dataset/train-images-idx3-ubyte.gz
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diff --git a/tests/assets/mnist_dataset/train-labels-idx1-ubyte.gz b/tests/assets/mnist_dataset/train-labels-idx1-ubyte.gz
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diff --git a/tests/test_mnist_csv_format.py b/tests/test_mnist_csv_format.py
new file mode 100644
index 000000000000..17286b90feb7
--- /dev/null
+++ b/tests/test_mnist_csv_format.py
@@ -0,0 +1,185 @@
+import os.path as osp
+from unittest import TestCase
+
+import numpy as np
+from datumaro.components.dataset import Dataset
+from datumaro.components.extractor import (AnnotationType, DatasetItem, Label,
+    LabelCategories)
+from datumaro.plugins.mnist_csv_format import (MnistCsvConverter,
+    MnistCsvImporter)
+from datumaro.util.image import Image
+from datumaro.util.test_utils import TestDir, compare_datasets
+
+
+class MnistCsvFormatTest(TestCase):
+    def test_can_save_and_load(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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_without_saving_images(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, subset='train',
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, subset='train',
+                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=False)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_with_different_image_size(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, image=np.ones((10, 8)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, image=np.ones((4, 3)),
+                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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id="кириллица с пробелом",
+                image=np.ones((28, 28)),
+                annotations=[Label(0)]
+            ),
+        ], 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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_image_with_arbitrary_extension(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id='q/1', image=Image(path='q/1.JPEG',
+                data=np.zeros((28, 28)))),
+            DatasetItem(id='a/b/c/2', image=Image(path='a/b/c/2.bmp',
+                data=np.zeros((28, 28)))),
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistCsvConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_empty_image(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id=0, annotations=[Label(0)]),
+            DatasetItem(id=1)
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistCsvConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_with_other_labels(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id=0, image=np.ones((28, 28)),
+                annotations=[Label(0)]),
+            DatasetItem(id=1, image=np.ones((28, 28)),
+                annotations=[Label(1)])
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                'label_%s' % label for label in range(2)),
+        })
+
+        with TestDir() as test_dir:
+            MnistCsvConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist_csv')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mnist_csv_dataset')
+
+class MnistCsvImporterTest(TestCase):
+    def test_can_import(self):
+        expected_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)),
+                annotations=[Label(2)]
+            ),
+            DatasetItem(id=2, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(1)]
+            ),
+            DatasetItem(id=0, subset='train',
+                image=np.ones((28, 28)),
+                annotations=[Label(5)]
+            ),
+            DatasetItem(id=1, subset='train',
+                image=np.ones((28, 28)),
+                annotations=[Label(7)]
+            )
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'mnist_csv')
+
+        compare_datasets(self, expected_dataset, dataset)
+
+    def test_can_detect(self):
+        self.assertTrue(MnistCsvImporter.detect(DUMMY_DATASET_DIR))
diff --git a/tests/test_mnist_format.py b/tests/test_mnist_format.py
new file mode 100644
index 000000000000..eb5f5299b167
--- /dev/null
+++ b/tests/test_mnist_format.py
@@ -0,0 +1,184 @@
+import os.path as osp
+from unittest import TestCase
+
+import numpy as np
+from datumaro.components.dataset import Dataset
+from datumaro.components.extractor import (AnnotationType, DatasetItem, Label,
+    LabelCategories)
+from datumaro.plugins.mnist_format import MnistConverter, MnistImporter
+from datumaro.util.image import Image
+from datumaro.util.test_utils import TestDir, compare_datasets
+
+
+class MnistFormatTest(TestCase):
+    def test_can_save_and_load(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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_without_saving_images(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, subset='train',
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, subset='train',
+                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=False)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_with_different_image_size(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id=0, image=np.ones((3, 4)),
+                annotations=[Label(0)]
+            ),
+            DatasetItem(id=1, image=np.ones((2, 2)),
+                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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
+        source_dataset = Dataset.from_iterable([
+            DatasetItem(id="кириллица с пробелом",
+                image=np.ones((28, 28)),
+                annotations=[Label(0)]
+            ),
+        ], 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)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, source_dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_image_with_arbitrary_extension(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id='q/1', image=Image(path='q/1.JPEG',
+                data=np.zeros((28, 28)))),
+            DatasetItem(id='a/b/c/2', image=Image(path='a/b/c/2.bmp',
+                data=np.zeros((28, 28)))),
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_empty_image(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id=0, annotations=[Label(0)]),
+            DatasetItem(id=1)
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        with TestDir() as test_dir:
+            MnistConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+    def test_can_save_and_load_with_other_labels(self):
+        dataset = Dataset.from_iterable([
+            DatasetItem(id=0, image=np.ones((28, 28)),
+                annotations=[Label(0)]),
+            DatasetItem(id=1, image=np.ones((28, 28)),
+                annotations=[Label(1)])
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                'label_%s' % label for label in range(2)),
+        })
+
+        with TestDir() as test_dir:
+            MnistConverter.convert(dataset, test_dir, save_images=True)
+            parsed_dataset = Dataset.import_from(test_dir, 'mnist')
+
+            compare_datasets(self, dataset, parsed_dataset,
+                require_images=True)
+
+DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'mnist_dataset')
+
+class MnistImporterTest(TestCase):
+    def test_can_import(self):
+        expected_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)),
+                annotations=[Label(2)]
+            ),
+            DatasetItem(id=2, subset='test',
+                image=np.ones((28, 28)),
+                annotations=[Label(1)]
+            ),
+            DatasetItem(id=0, subset='train',
+                image=np.ones((28, 28)),
+                annotations=[Label(5)]
+            ),
+            DatasetItem(id=1, subset='train',
+                image=np.ones((28, 28)),
+                annotations=[Label(7)]
+            )
+        ], categories={
+            AnnotationType.label: LabelCategories.from_iterable(
+                str(label) for label in range(10)),
+        })
+
+        dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'mnist')
+
+        compare_datasets(self, expected_dataset, dataset)
+
+    def test_can_detect(self):
+        self.assertTrue(MnistImporter.detect(DUMMY_DATASET_DIR))