Annotation Converters

Annotation converter is a function which converts annotation file to suitable for metric evaluation format. Each annotation converter expects specific annotation file format or data structure, which depends on original dataset. If converter for your data format is not supported by Accuracy Checker, you can provide your own annotation converter. Each annotation converter has parameters available for configuration.

Process of conversion can be implemented in two ways:

• via configuration file

• via command line

Describing Annotation Conversion in Configuration File

Annotation conversion can be provided in dataset section of your configuration file to convert annotation in-place before every evaluation. Each conversion configuration should contain converter field filled with a selected converter name and provide converter specific parameters (more details in supported converters section). All paths can be prefixed via command line with -s, --source argument.

You can additionally use optional parameters like:

• subsample_size - Dataset subsample size. You can specify the number of ground truth objects or dataset ratio in percentage. Please, be careful to use this option, some datasets does not support subsampling. You can also specify subsample_seed if you want to generate subsample with specific random seed.

• annotation - path to store converted annotation pickle file. You can use this parameter if you need to reuse converted annotation to avoid subsequent conversions.

• dataset_meta - path to store meta information about converted annotation if it is provided.

• analyze_dataset - flag which allow getting statistics about the converted dataset. Supported annotations: ClassificationAnnotation, DetectionAnnotation, MultiLabelRecognitionAnnotation, RegressionAnnotation. Default value is False.

Example of usage:

annotation_conversion:
  # Converter name which will be called for conversion.
  converter: sample
  # Converter specific parameters, can be different depend on converter realization.
  data_dir: sample/sample_dataset
# (Optional) subsample generation. Can be also used with prepared annotation file.
subsample_size: 1000
# (Optional) paths to store annotation files for following usage. In the next evaluation these files will be directly used instead running conversion.
annotation: sample_dataset.pickle
dataset_meta: sample_dataset.json

Conversing Process via Command Line

The command line for annotation conversion looks like:

convert_annotation <converter_name> <converter_specific parameters>

All converter specific options should have format --<parameter_name> <parameter_value> You may refer to -h, --help to full list of command line options. Some optional arguments are:

• -o, --output_dir - directory to save converted annotation and meta info.

• -a, --annotation_name - annotation file name.

• -m, --meta_name - meta info file name.

Supported Converters

Accuracy Checker supports following list of annotation converters and specific for them parameters:

• cifar - converts CIFAR classification dataset to ClassificationAnnotation

  • data_batch_file - path to pickle file which contain dataset batch (e.g. test_batch).

  • batch_meta_file - path to pickle file which contains label names (e.g. batches.meta).

  • has_background - allows to add background label to original labels (Optional, default value is False).

  • convert_images - allows converting images from pickle file to user specified directory (default value is False).

  • converted_images_dir - path to converted images location.

  • num_classes - the number of classes in the dataset - 10 or 100 (Optional, default 10)

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• mnist - convert MNIST dataset for handwritten digit recognition to ClaassificationAnnotation. Dataset can be downloaded here.

  • labels_file - binary file which contains labels.

  • images_file - binary file which contains images.

  • convert_images - allows converting images from data file to user specified directory (default value is False).

  • converted_images_dir - path to converted images location if enabled convert_images.

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding). Optional, more details in Customizing dataset meta section.

• mnist_csv - convert MNIST dataset for handwritten digit recognition stored in csv format to ClassificationAnnotation.

  • annotation_file - path to dataset file in csv format.

  • convert_images - allows converting images from annotation file to user specified directory (default value is False).

  • converted_images_dir - path to converted images location if enabled convert_images.

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding). Optional, more details in Customizing dataset meta section.

• fashion_mnist - convert Fashion-MNIST dataset to ClassificationAnnotation.

  • annotation_file - path to labels file in a binary format.

  • data_file - path to images file in a binary format.

  • convert_images - allows converting images from data file to user specified directory (default value is False).

  • converted_images_dir - path to converted images location if enabled convert_images.

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding). Optional, more details in Customizing dataset meta section.

• imagenet - convert ImageNet dataset for image classification task to ClassificationAnnotation.

  • annotation_file - path to annotation in txt format.

  • labels_file - path to file with word description of labels (synset_words).

  • has_background - allows adding background label to original labels and convert dataset for 1001 classes instead 1000 (default value is False).

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• voc_detection - converts Pascal VOC annotation for detection task to DetectionAnnotation.

  • imageset_file - path to file with validation image list.

  • annotations_dir - path to directory with annotation files.

  • images_dir - path to directory with images related to devkit root (default JPEGImages).

  • has_background - allows convert dataset with/without adding background_label. Accepted values are True or False. (default is True)

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• voc_segmentation - converts Pascal VOC annotation for semantic segmentation task to SegmentationAnnotation.

  • imageset_file - path to file with validation image list.

  • images_dir - path to directory with images related to devkit root (default JPEGImages).

  • mask_dir - path to directory with ground truth segmentation masks related to devkit root (default SegmentationClass).

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section. Note : Since OpenVINO 2020.4 the converter behaviour changed. data_source parameter of dataset should contains directory for images only, if you have segmentation mask in separated location, please use segmentation_masks_source for specifying gt masks location.

• mscoco_detection - converts MS COCO dataset for object detection task to DetectionAnnotation.

  • annotation_file - path to annotation file in json format.

  • has_background - allows convert dataset with/without adding background_label. Accepted values are True or False. (default is False).

  • use_full_label_map - allows using original label map (with 91 object categories) from paper instead public available(80 categories).

  • sort_annotations - allows saving annotations in a specific order: ascending order of image id or ascending order of image size.

  • sort_key - key by which annotations will be sorted(supported keys are image_id and image_size, default is image_id).

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

  • convert_COCO_to_VOC_labels - allows to convert COCO labels to Pacsal VOC labels. Optional, default is False.

• mscoco_segmentation - converts MS COCO dataset for object instance segmentation task to CocoInstanceSegmentationAnnotation.

  • annotation_file - path to annotation file in json format.

  • has_background - allows convert dataset with/without adding background_label. Accepted values are True or False. (default is False).

  • use_full_label_map - allows using original label map (with 91 object categories) from paper instead public available(80 categories).

  • sort_annotations - allows saving annotations in a specific order: ascending order of image id or ascending order of image size.

  • sort_key - key by which annotations will be sorted (supported keys are image_id and image_size, default is image_id).

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

  • semantic_only - converts MS COCO dataset annotation to SegmentationAnnotation. (Optional, default value is False)

  • masks_dir - path to store segmentation masks in semantic_only mode

  • convert_COCO_to_VOC_labels - allows to convert COCO labels to Pacsal VOC labels. Optional, default is False.

• mscoco_mask_rcnn - converts MS COCO dataset to ContainerAnnotation with DetectionAnnotation and CocoInstanceSegmentationAnnotation named detection_annotation and segmentation_annotation respectively.

  • annotation_file - path to annotation file in json format.

  • has_background - allows convert dataset with/without adding background_label. Accepted values are True or False. (default is False).

  • use_full_label_map - allows using original label map (with 91 object categories) from paper instead public available(80 categories).

  • sort_annotations - allows saving annotations in a specific order: ascending order of image id or ascending order of image size.

  • sort_key - key by which annotations will be sorted (supported keys are image_id and image_size, default is image_id).

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

  • convert_COCO_to_VOC_labels - allows to convert COCO labels to Pacsal VOC labels. Optional, default is False.

• mscoco_keypoints - converts MS COCO dataset for keypoints localization task to PoseEstimationAnnotation.

  • annotation_file - path to annotation file in json format.

  • sort_annotations - allows saving annotations in a specific order: ascending order of image id or ascending order of image size.

  • sort_key - key by which annotations will be sorted (supported keys are image_id and image_size, default is image_id).

  • remove_empty_images - allows excluding/inclusing images without objects from/to the dataset..

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• wider - converts from Wider Face dataset to DetectionAnnotation.

  • annotation_file - path to txt file, which contains ground truth data in WiderFace dataset format.

  • label_start - specifies face label index in label map. Default value is 1. You can provide another value, if you want to use this dataset for separate label validation, in case when your network predicts other class for faces.

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• detection_opencv_storage - converts detection annotation stored in Detection OpenCV storage format to DetectionAnnotation.

  • annotation_file - path to annotation in xml format.

  • image_names_file - path to txt file, which contains image name list for dataset.

  • label_start - specifies label index start in label map. Default value is 1. You can provide another value, if you want to use this dataset for separate label validation.

  • background_label - specifies which index will be used for background label. You can not provide this parameter if your dataset has not background label.

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• cityscapes - converts CityScapes Dataset to SegmentationAnnotation.

  • dataset_root_dir - path to dataset root.

  • images_subfolder - path from dataset root to directory with validation images (Optional, default imgsFine/leftImg8bit/val).

  • masks_subfolder - path from dataset root to directory with ground truth masks (Optional, gtFine/val).

  • masks_suffix - suffix for mask file names (Optional, default _gtFine_labelTrainIds).

  • images_suffix - suffix for image file names (Optional, default _leftImg8bit).

  • use_full_label_map - allows using full label map with 33 classes instead train label map with 18 classes (Optional, default False).

  • dataset_meta_file - path to json file with a dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• mapillary_20 - converts Mapillary dataset contained 20 classes to SegmentationAnnotation.

  • data_dir - path to dataset root folder. Relative paths to images and masks directory determine as imgs and masks respectively. In way when images and masks are located in non default directories, you can use parameters described below.

  • images_dir - path to images folder.

  • mask_dir - path to ground truth mask folder.

  • images_subfolder - sub-directory for images(Optional, default imgs)

  • mask_subfolder - sub-directory for ground truth mask(Optional, default masks)

• mapillary_vistas - converts Mapillary Vistas dataset contained 20 classes to SegmentationAnnotation.

  • data_dir - path to dataset root folder. Relative paths to images and masks directory determine as images and labels respectively. In way when images and masks are located in non default directories, you can use parameters described below.

  • images_dir - path to images folder.

  • mask_dir - path to ground truth mask folder.

  • images_subfolder - sub-directory for images(Optional, default images)

  • mask_subfolder - sub-directory for ground truth mask(Optional, default labels)

• vgg_face - converts VGG Face 2 dataset for facial landmarks regression task to FacialLandmarksAnnotation.

  • landmarks_csv_file - path to csv file with coordinates of landmarks points.

  • bbox_csv_file - path to cvs file which contains bounding box coordinates for faces (optional parameter).

• lfw - converts Labeled Faces in the Wild dataset for face reidentification to ReidentificationClassificationAnnotation.

  • pairs_file - path to file with annotation positive and negative pairs.

  • train_file - path to file with annotation positive and negative pairs used for network train (optional parameter).

  • landmarks_file - path to file with facial landmarks coordinates for annotation images (optional parameter).

  • extension - images extension(optional, default - jpg).

• face_recognition_bin - converts preprocessed face recognition dataset stored in binary format to ReidentificationClassificationAnnotation.

  • bin_file - file with a dataset. Example of datasets can be found here.

  • images_dir - directory for saving converted images (Optional, used only if convert_images enabled, if not provided <dataset_root>/converted_images will be used)

  • convert_images - allows decode and save images.

• mars - converts MARS person reidentification dataset to ReidentificationAnnotation.

  • data_dir - path to data directory, where gallery (bbox_test) and query subdirectories are located.

• market1501_reid - converts Market1501 person reidentification dataset to ReidentificationAnnotation.

  • data_dir - path to data directory, where gallery (bounding_box_test) and query subdirectories are located.

• veri776_reid - converts VeRi776 vehicle reidentification dataset to ReidentificationAnnotation.

  • data_dir - path to data directory, where gallery (image_test) and image_query subdirectories are located.

• image_processing - converts dataset for common single image processing tasks (e.g. image denoising, style transferring) to ImageProcessingAnnotation. This converter is suitable for tasks where model output produced on specific input image should be compared with target image.

  • data_dir - path to folder, where images in low and high resolution are located.

  • input_suffix - input file name’s suffix (default in).

  • target_suffix - target ground truth file name’s suffix (default out).

  • recursive - enables acquiring of dataset files from data_dir subcatalogs (default False).

  • annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

• image_generation - converts dataset for generation images from noise to ImageProcessingAnnotation,

  • data_dir - dataset root directory where pregenerated input sequences are located in subdirectories.

  • input_subdirectories - list of relative paths for input data. If model have multiple inputs, data for each input should be located in separated directory.

  • reference_dir - directory with reference data. annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

• parametric_image_processing - converts dataset for image processing which required variable conditions for getting result, to ImageProcessingAnnotation. Parameters provided as float value in reference image name using _ as delimeter.

  • input_dir - directory with input images.

  • reference_dir - directory with reference images.

  • annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

  • param_scale - multiplayer for parameters (Optional, default 0.001).

• super_resolution - converts dataset for single image super resolution task to SuperResolutionAnnotation.

  • data_dir - path to folder, where images in low and high resolution are located.

  • lr_dir - path to directory, where images in low resolution are located.

  • hr_dir - path to directory, where images in high resolution are located. Note: inside converted annotation, path to directory is not stored, only file name, please use additional_data_source for providing prefix.

  • upsampled_dir - path to directory, where upsampled images are located, if 2 streams used.

  • ignore_suffixes - matched low resolution, high resolution image located in different directories without usage suffixes, using numeric ids (Optional, default False).

  • lr_suffix - low resolution file name’s suffix (default lr).

  • hr_suffix - high resolution file name’s suffix (default hr).

  • annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow, pillow_rgb (for explicit data conversion to RGB format), dicom, skimage. (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

  • two_streams - enable 2 input streams where usually first for an original image and second for upsampled image. (Optional, default False).

  • upsample_suffix - upsample images file name’s suffix (default upsample).

• super_resolution_dir_based - converts dataset for single image super resolution task to SuperResolutionAnnotation which have directory based structure (high resolution and low resolution images located on separated directories and matches by name or numeric id). The main difference between this converter and super_resolution in data organization. super_resolution converter should be used if all high and low resolution images located in the same dir and have difference in suffixes.

  • annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow, pillow_rgb (for explicit data conversion to RGB format), dicom, skimage. (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

  • two_streams - enable 2 input streams where usually first for original image and second for upsampled image. (Optional, default False).

  • images_dir - path to dataset root, where directories with low and high resolutions are located.

  • lr_dir - path to directory, where images in low resolution are located (Optional, default <images_dir>/LR).

  • hr_dir - path to directory, where images in high resolution are located (Optional, default <images_dir>/HR). Note: inside converted annotation, path to directory is not stored, only file name, please use additional_data_source for providing prefix.

  • upsampled_dir - path to directory, where upsampled images are located, if 2 streams used (Optional, default <images_dir>/upsample).

  • relaxed_names - allow using more relaxed search of high resolution or/and upsampled images matching only numeric ids. Optional, by default full name matching required.

  • hr_prefixed - allow using partial name matching when low resolution filename is a part of high resolution filename. Not applicable when relaxed_names is set. Optional, by default full name matching required.

• multi_frame_super_resolution - converts dataset for super resolution task with multiple input frames usage.

  • data_dir - path to folder, where images in low and high resolution are located.

  • lr_suffix - low resolution file name’s suffix (default lr).

  • hr_suffix - high resolution file name’s suffix (default hr).

  • annotation_loader - which library will be used for ground truth image reading. Supported: opencv, pillow (Optional. Default value is pillow). Note, color space of image depends on loader (OpenCV uses BGR, Pillow uses RGB for image reading).

  • number_input_frames - the number of input frames per inference.

  • reference_frame - the id of frame in sample frame sequence used for matching with high resolution. You can define number of frame or choose one of predefined: first (first frame used as reference), middle (num_frames / 2), last (last frame in sequence).

• multi_target_super_resolution - converts dataset for single image super resolution task with multiple target resolutions to ContainerAnnotation with SuperResolutionAnnotation representations for each target resolution.

  • data_dir - path to dataset root, where directories with low and high resolutions are located.

  • lr_path - path to low resolution images directory relative to data_dir.

  • hr_mapping - dictionary which represent mapping between target resolution and directory with images. Keys are also used as keys for ContainerAnnotation. All paths should be relative to data_dir.

• icdar_detection - converts ICDAR13 and ICDAR15 datasets for text detection challenge to TextDetectionAnnotation.

  • data_dir - path to folder with annotations on txt format.

  • word_spotting - if it is true then transcriptions that have lengths less than 3 symbols or transcriptions containing non-alphanumeric symbols will be marked as difficult.

• icdar13_recognition - converts ICDAR13 dataset for text recognition task to CharacterRecognitionAnnotation.

  • annotation_file - path to annotation file in txt format.

  • delimeter - delimeter between image and text for recognition. Supported values - space and tab for space and tabular separator respectively.

• lmdb_text_recognition_database - converter for text recognition dataset in a form of LMDB database.

  • lower_case - parameter describing if ground truth text should be converted to lower case.

• unicode_character_recognition - converts Kondate dataset and Nakayosi for handwritten Japanese text recognition task , and SCUT-EPT for handwritten simplified Chinese text recognition task to CharacterRecognitionAnnotation.

  • annotation_file - path to annotation file in txt format.

  • decoding_char_file - path to decoding_char_file, consisting of all supported characters separated by ‘

    ‘ in txt format.

• bentham_lines - converts Bentham dataset for line-level character recognition to CharacterRecognitionAnnotation.

  • transcription_dir - directory stored line transcriptions

  • partition_file - file with selected subset for validation.

  • normalize_text - allow unicode normalization for text (Optional, default False).

  • to_lower - converts transcription text to lower case (Optional, default False).

• brats - converts BraTS dataset format to BrainTumorSegmentationAnnotation format. Also, can be used to convert other nifti-based datasets.

  • data_dir - dataset root directory, which contain subdirectories with validation data (imagesTr) and ground truth labels (labelsTr). Optionally you can provide a relative path for these subdirectories (if they have different location) using image_folder and mask_folder parameters respectively.

  • mask_channels_first - allows read gt mask nifti files and transpose in order where channels first (Optional, default False)

  • labels_file - path to file, which contains labels (optional, if omitted no labels will be shown)

  • relaxed_names - allows using more relaxed search of labels matching only numeric ids. Optional, by default full name matching required.

  • multi_frame - allows converting annotation of 3D images as sequence of 2D frames (optional, default False)

  • frame_separator - string separator between file name and frame number in multi_frame (optional, default #)

  • frame_axis - number of frame axis in 3D Image (optional, default -1, last axis)

  • as_regression - allows dataset conversion as NiftiRegressionAnnotation annotation (optional, default False)

• k_space_mri - converts k-spaced MRI dataset format to ImageRepresentationAnnotation format. MRI datasets, for example Calgary-Campinas, provides data in Fourier images form (so called k-space images). Converter performs dataset annotation and preprocessing of ground truth images and model input.

  • data_dir - path to dataset root

  • image_folder - path to source k-space files directory, relatively data_dir (optional, default images)

  • reconstructed_folder - path to reconstructed images directory, relatively data_dir (optional, default reconstructed)

  • masked_folder - path to masked k-space files directory, relatively data_dir (optional, default masked)

  • mask_file - k-space mask filename

  • stats_file - k-space normalization factors filename

  • skip_dumps - allows dataset annotation without preprocessing

• movie_lens_converter - converts Movie Lens Datasets format to HitRatioAnnotation format.

  • rating_file - path to file which contains movieId with top score for each userID (for example ml-1m-test-ratings.csv)

  • negative_file - path to file which contains negative examples.

  • users_max_number - the number of users which will be used for validation (Optional, it gives opportunity to cut list of users. If argument is not provided, full list of users will be used.).

• brats_numpy - converts Brain Tumor Segmentation dataset to BrainTumorSegmentationAnnotation. This converter works with Numpy representation of BraTS dataset.

  • data_dir - path to dataset root directory.

  • ids_file - path to file, which contains names of images in dataset

  • labels_file - path to file, which contains labels (optional, if omitted no labels will be shown)

  • data_suffix - suffix for files with data (default _data_cropped)

  • label_suffix - suffix for files with groundtruth data (default _label_cropped)

  • boxes_file - path to file with brain boxes (optional). Set this option with including postprocessor segmentation-prediction-resample (see Postprocessors).

• wmt - converts WMT dataset for Machine Translation task to MachineTranslationAnnotation.

  • input_file - path to file which contains input sentences tokens for translation.

  • reference_file - path to file with reference for translation.

• common_semantic_segmentation - converts a general format of datasets for semantic segmentation task to SegmentationAnnotation. The converter expects following dataset structure:

  1. images and GT masks are located in separated directories (e.g. <dataset_root>/images for images and <dataset_root>/masks for masks respectively)

  2. images and GT masks has common part in names and can have difference in prefix and postfix (e.g. image name is image0001.jpeg, mask for it is gt0001.png are acceptable. In this case base_part - 0001, image_prefix - image, image_postfix - .jpeg, mask_prefix - gt, mask_postfix - .png)

  • images_dir - path to directory with images.

  • masks_dir - path to directory with GT masks.

  • image_prefix - prefix part for image file names. (Optional, default is empty).

  • image_postfix - postfix part for image file names (optional, default is .png).

  • mask_prefix - prefix part for mask file names. (Optional, default is empty).

  • image_postfix - postfix part for mask file names (optional, default is .png).

  • mask_loader - the way how GT mask should be loaded. Supported methods: pillow, opencv, nifti, numpy, scipy.

  • dataset_meta_file - path to json file with prepared dataset meta info. It should contain label_map key with dictionary in format class_id: class_name and optionally segmentation_colors (if your dataset uses color encoding). Segmentation colors is a list of channel-wise values for each class. (e.g. if your dataset has 3 classes in BGR colors, segmentation colors for it will looks like: [[255, 0, 0], [0, 255, 0], [0, 0, 255]]). (Optional, you can provide self-created file as dataset_meta in your config). Note: since OpenVINO 2020.4 converter behaviour changed. data_source parameter of dataset should contain the directory for images only, if you have segmentation mask in separated location, please use segmentation_masks_source for specifying gt masks location.

• background_matting - converts a general format of datasets for background matting task to BackgroundMattingAnnotation. The converter expects following dataset structure:

  1. images and GT masks are located in separated directories (e.g. <dataset_root>/images for images and <dataset_root>/masks for masks respectively)

  2. images and GT masks has common part in names and can have difference in prefix and postfix (e.g. image name is image0001.jpeg, mask for it is gt0001.png are acceptable. In this case base_part - 0001, image_prefix - image, image_postfix - .jpeg, mask_prefix - gt, mask_postfix - .png)

  • images_dir - path to directory with images.

  • masks_dir - path to directory with GT masks.

  • image_prefix - prefix part for image file names. (Optional, default is empty).

  • image_postfix - postfix part for image file names (optional, default is .png).

  • mask_prefix - prefix part for mask file names. (Optional, default is empty).

  • image_postfix - postfix part for mask file names (Optional, default is .png).

  • mask_to_gray - allows casting matting mask to gray scale (Optional, default False).

• video_background_matting - converts a general format of datasets, where frames extracted from video for background matting task to BackgroundMattingAnnotation. Belonging image to video should be reflected into its name <video_name>.mp4.<image_name>.jpg. The converter expects following dataset structure:

  1. images and GT masks are located in separated directories (e.g. <dataset_root>/images for images and <dataset_root>/masks for masks respectively).

  2. images and GT masks has common part in names and can have difference in prefix and postfix (e.g. image name is image0001.jpeg, mask for it is gt0001.png are acceptable. In this case base_part - 0001, image_prefix - image, image_postfix - .jpeg, mask_prefix - gt, mask_postfix - .png)

  • images_dir - path to directory with images.

  • masks_dir - path to directory with GT masks.

  • image_prefix - prefix part for image file names. (Optional, default is empty).

  • image_postfix - postfix part for image file names (optional, default is .png).

  • mask_prefix - prefix part for mask file names. (Optional, default is empty).

  • image_postfix - postfix part for mask file names (Optional, default is .png).

  • mask_to_gray - allows casting matting mask to gray scale (Optional, default False).

• background_matting_sequential - converts a general format of datasets for background matting task to BackgroundMattingAnnotation. Images and GT data are grouoed by clips. The converter expects following dataset structure:

  1. images, GT masks, backgrounds and foregrounds if needed are located in separated directories (e.g. <dataset_root>/images for images, <dataset_root>/masks for masks, <dataset_root>/backgrounds for backgrounds and <dataset_root>/foregrounds for foregrounds respectively).

  2. images, GT masks, backgrounds and foregrounds has common part in names and can have difference in prefix and postfix (e.g. image name is clip_0/image0001.jpeg, mask for it is clip_0/gt0001.png, background is clip_0/bg0001.png, foreground is clip_0/fgr0001.png are acceptable. In this case base_part - 0001, image_prefix - image, image_postfix - .jpeg, mask_prefix - gt, mask_postfix - .png, background_prefix - bg, background_postfix - .png, foreground_prefix - fgr, foreground_postfix - .png)

  • images_dir - path to directory with images.

  • masks_dir - path to directory with GT masks.

  • image_prefix - prefix part for image file names. (Optional, default is empty).

  • image_postfix - postfix part for image file names (optional, default is .png).

  • mask_prefix - prefix part for mask file names (Optional, default is empty).

  • mask_postfix - postfix part for mask file names (Optional, default is .png).

  • mask_to_gray - allows casting matting mask to gray scale (Optional, default False).

  • backgrounds_dir - path to gt backgrounds directory.

  • background_prefix - prefix for gt backgrounds.

  • background_postfix - postfix for gt backgrounds.

  • with_background - load backgrounds.

  • foregrounds_dir - path to gt foregrounds directory (Optional, default is None).

  • foreground_prefix - prefix for gt foregrounds.

  • foreground_postfix - postfix for gt foregrounds.

  • with_foreground - load foregrounds.

  • with_alpha - load images with mask including alpha channel.

• camvid - converts CamVid dataset with 12 classes to SegmentationAnnotation. Dataset can be found in the following repository

  • annotation_file - file in txt format which contains list of validation pairs (<path_to_image> <path_to_annotation> separated by space)

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• camvid_32 - converts CamVid dataset with 32 classes to SegmentationAnnotation. Dataset can be found here.

  • labels_dir - directory with labeled ground truth images.

  • images_dir - directory with input data.

  • val_subset_ratio - ratio of subset, which should be used for validation. It is the float value in (0, 1] range for definition subset size as <total_dataset_size> \* <subset_ratio>. Optional, default 1 (it means full dataset used for validation).

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• image_retrieval - converts dataset for image retrieval task to ReidentificationAnnotation. Dataset should have following structure:

  1. the dataset root directory contains 2 subdirectory named gallery and queries for gallery images and query images respectively.

  2. Every of these subdirectories should contains text file with list of pairs: <path_to_image> <image_ID> (image_path and image_ID should be separated by space), where <path_to_image> is path to the image related dataset root, <image_ID> is the number which represent image id in the gallery.

  • data_dir - path to dataset root directory.

  • gallery_annotation_file - file with gallery images and IDs concordance in txt format (Optional, default value is <data_dir>/gallery/list.txt)

  • queries_annotation_file - file with queries images and IDs concordance in txt format (Optional, default value is <data_dir>/queries/list.txt)

• cvat_object_detection - converts CVAT XML annotation version 1.1 format for images to DetectionAnnotation.

  • annotation_file - path to xml file in appropriate format.

  • has_background - allows to prepend original labels with special class represented background and convert dataset for n+1 classes instead n (default value is True).

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in Customizing dataset meta section.

• cvat_attributes_recognition - converts CVAT XML annotation version 1.1 format for images to ClassificationAnnotation or ContainerAnnotation with ClassificationAnnotation as value type and attribute names as keys (in multiple attributes case). Used bbox attributes as annotation classes.

  • annotation_file - path to xml file in appropriate format.

  • label - the dataset label which will be used for attributes collection (e.g. if your dataset contains 2 labels: face and person and you want recognise attributes for face, you should use face as value for this parameter).

• cvat_age_gender - converts CVAT XML annotation version 1.1 format for images which represent dataset for age gender recognition to ContainerAnnotation with ClassificationAnnotation for gender recognition, ClassificationAnnotation for age classification and RegeressionAnnotation for age regression. The identifiers for representations following: gender_annotation, age_class_annotation, age_regression_annotation.

  • annotation_file - path to xml file in appropriate format.

• cvat_facial_landmarks - converts CVAT XML annotation version 1.1 format for images to FacialLandmarksAnnotation.

• coco_facial_landmarks - converts landmarks dataset in Coco format to FacialLandmarksHeatMapAnnotation.

  • annotation_file - path to dataset annotation file in Coco format

• cvat_facial_landmarks - converts CVAT XML annotation version 1.1 format for images to FacialLandmarksAnnotation.

  • annotation_file - path to xml file in appropriate format.

• cvat_pose_estimation - converts CVAT XML annotation version 1.1 format for images to PoseEstimationAnnotation.

  • annotation_file - path to xml file in appropriate format.

• cvat_text_recognition - converts CVAT XML annotation version 1.1 format for images to CharacterRecognitionAnnotation.

  • annotation_file - path to xml file in appropriate format.

• cvat_binary_multilabel_attributes_recognition - converts CVAT XML annotation version 1.1 format for images to MultiLabelRecognitionAnnotation. Used bbox attributes as annotation classes. Each attribute field should contains T or F values for attribute existence/non-existence on the image respectively.

  • annotation_file - path to xml file in appropriate format.

  • label - the dataset label which will be used for attributes collection (e.g. if your dataset contains 2 labels: face and person and you want recognise attributes for face, you should use face as value for this parameter).

• cvat_person_detection_action_recognition converts dataset with CVAT XML annotation version 1.1 for person detection and action recognition task to ContainerAnnotation with DetectionAnnotation for person detection quality estimation named person_annotation and ActionDetectionAnnotation for action recognition named action_annotation.

  • annotation_file - path to xml file with ground truth.

  • use_case - use case, which determines the dataset label map. Supported range actions:

    • common_3_actions (seating, standing, raising hand)

    • common_6_actions (seating, writing, raising hand, standing, turned around, lie on the desk)

    • teacher (standing, writing, demonstrating)

    • raising_hand (seating, raising hand)

• lpr_txt - converts annotation for license plate recognition task in txt format to CharacterRecognitionAnnotation.

  • annotation_file - path to txt annotation.

  • decoding_dictionary - path to file containing dictionary for output decoding.

• squad_emb - converts the Stanford Question Answering Dataset (SQuAD) to Question Answering Embedding Annotation. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • testing_file - path to testing file.

  • vocab_file - path to model co vocabulary file.

  • class_token_first - Add [CLS] token to the begin of sequence. If False, will be added as the last token.

  • enable_padding - pad input sequence to max length.

  • tokenizer_dir - path to a directory containing vocabulary files required by the transformers tokenizer

  • model_id - model id of a predefined tokenizer hosted inside a model repo on huggingface.co.

  • lower_case - converts output to lower case.

  • max_seq_length - maximum total input sequence length after word-piece tokenization (Optional, default value is 128).

  • max_query_length - maximum number of tokens for the question (Optional, default value is 64).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False)

  • enable_padding - enable padding inputs to max length for queries and sequences.

• squad - converts the Stanford Question Answering Dataset (SQuAD) to Question Answering Annotation. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • testing_file - path to testing file.

  • vocab_file - path to model co vocabulary file.

  • max_seq_length - maximum total input sequence length after word-piece tokenization (Optional, default value is 128).

  • max_query_length - maximum number of tokens for the question (Optional, default value is 64).

  • doc_stride -stride size between chunks for splitting up long document (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False)

  • enable_padding - enable padding for max sequence length.

• squad_bidaf - converts the Stanford Question Answering Dataset (SQuAD) to QuestionAnsweringBiDAFAnnotation. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for BiDAF using nltk.word_tokenize.

  • testing_file - path to testing file.

• xnli - converts The Cross-lingual Natural Language Inference Corpus (XNLI) to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization (Optional in case, when another tokenization approach used).

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after word-piece tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • language_filter - comma-separated list of used in annotation language tags for selecting records for specific languages only. (Optional, if not used full annotation will be converted).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• mnli - converts The Multi-Genre Natural Language Inference Corpus (MNLI) to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• mrpc - converts The Microsoft Research Paraphrase Corpus (MRPC) to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• cola - converts The Corpus of Linguistic Acceptability (CoLA) to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• wnli - converts Winograd NLI dataset (WNLI) to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base model should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• qnli - converts Question NLI dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base model should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• qqp - converts Quora Question Pairs dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for BERT.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base model should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• sst-2 - converts Stanford Sentiment Treebank SST-2 dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base model should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• rte - converts Recognizing Textual Entailment RTE dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base model should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• imdb - converts IMDB sentiment dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv format.

  • vocab_file - path to model vocabulary file for WordPiece tokenization. (Optional, can be not provided in case, when another tokenization approach used.)

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• custom_text_classification - converts Custom Column dataset to TextClassificationAnnotattion. Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.

  • annotation_file - path to dataset annotation file in tsv or csv format.

  • separator - column separator, supported values: comma for comma-separated data and tab - for tabular separation. Optional, default behaviour derived from file extension - for csv is ,, for tsv - \t.

  • text_1 - column id for text (Optional, default - 0).

  • text_2 - column id for text for sentence pair classification task (Optional, does not used if not provided).

  • label - column id for label (Optional, default - 1).

  • labels_list - list of label names (Optional, can be also provided using dataset_meta_file),

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map).Optional, more details in Customizing dataset meta section.

  • vocab_file - path to model vocabulary file for WordPiece tokenization (Optional in case, when another tokenization approach used).

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after word-piece tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

    tokenizer_dir: <model_saving_path>/roberta-base

• sentence_similarity - converts Column-based dataset for sentence similarity evaluation to SentenceSimilarityAnnotation.**Note: This converter not only converts data to metric specific format but also tokenize and encodes input for model.**

  • annotation_file - path to dataset annotation file in tsv or csv format.

  • separator - column separator, supported values: comma for comma-separated data and tab - for tabular separation. Optional, default behaviour derived from file extension - for csv is ,, for tsv - \t.

  • text_1 - column id for text (Optional, default - 0).

  • text_2 - column id for text for sentence pair classification task (Optional, default - 1).

  • label - column id for label gold score (Optional, default - 2).

  • vocab_file - path to model vocabulary file for WordPiece tokenization (Optional in case, when another tokenization approach used).

  • sentence_piece_model_file - model used for SentencePiece tokenization (Optional in case, when another tokenization approach used).

  • max_seq_length - maximum total input sequence length after word-piece tokenization (Optional, default value is 128).

  • lower_case - allows switching tokens to lower case register. It is useful for working with uncased models (Optional, default value is False).

  • enable_padding - pad sequence to maximum sequence length (Optional, default True). You also can use HuggingFace Transformers library capabilities for tokenization with providing model_id for downloading tokenizer files from huggingface.co or tokenizer_dir for specification pretrained model directory instead vocab_file or sentencepiece model. e.g. using tokenizer for roberta-base should be

    model_id: roberta-base

    or

            tokenizer_dir: <model_saving_path>/roberta-base
    \* `bert_xnli_tf_record` - converts The Cross-lingual Natural Language Inference Corpus ([XNLI](https://github.com/facebookresearch/XNLI)) stored in tf records format. This converter usage requires TensorFlow installation. Please make sure that TensorFlow installed before conversion.
      \* `annotattion_file` - path to annotation file in tf records format.
    \* `cmu_panoptic_keypoints` - converts CMU Panoptic dataset to `PoseEstimation3dAnnotation` format.
      \* `data_dir` - dataset root directory, which contain subdirectories with validation scenes data.
    \* `clip_action_recognition` - converts annotation video-based action recognition datasets. Before conversion validation set should be preprocessed using approach described [here](https://github.com/openvinotoolkit/training_extensions/blob/misc/models/action_recognition/model_templates/custom-action-recognition/README.md#3-prepare-data).
      \* `annotation_file` - path to annotation file in json format.
      \* `data_dir` - path to directory with prepared data (e. g. data/kinetics/frames_data).
      \* `clips_per_video` - number of clips per video (Optional, default 3).
      \* `clip_duration` - clip duration (Optional, default 16)
      \* `temporal_stride` - temporal stride for frames selection (Optional, default 2).
      \* `numpy_input` - allows usage numpy files instead images. It can be useful if data required difficult preprocessing steps (e.g. conversion to optical flow) (Optional, default `False`)
      \* `subset` - dataset split: `train`, `validation` or `test` (Optional, default `validation`).
      \* `dataset_meta_file` - path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in [Customizing dataset meta](#customizing-dataset-meta) section.
      \* `num_samples` - select first n samples from dataset (Optional, if not provided full subset of samples will be used).
    \* `continuous_clip_action_recognition` - converts annotation of video-based MS-ASL dataset to `ClassificationAnnotation`.
      \* `annotation_file` - path to annotation file in txt format.
      \* `data_dir` - dataset root directory, which contains subdirectories with extracted video frames.
      \* `out_fps` - output frame rate of generated video clips.
      \* `clip_length` - number of frames of generated video clips.
      \* `img_prefix` - prefix for used images. (Optional, default - `img_`).
    \* `redweb` - converts [ReDWeb](https://sites.google.com/site/redwebcvpr18) dataset for monocular relative depth perception to `DepthEstimationAnnotation`
      \* `data_dir` - the dataset root directory, where `imgs` - directory with RGB images and `RD` - directory with relative depth maps are located (Optional, if you want to provide `annotation_file`)
      \* `annotation_file`- the file in txt format which contains pairs of image and depth map files. (Optional, if not provided full content of `data_dir` will be considered as dataset.)
    \* `nyu_depth_v2` - converts [NYU Depth Dataset V2](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) for depth estimation to `DepthEstimationAnnotation`. This converter accept preprocessed data stored in HDF5 format, which can be downloaded from this [page](http://datasets.lids.mit.edu/fastdepth/data/)
      \* `data_dir` - directory with HDF5 files. (Optional, can be omitted if you already have converted images and depth maps).
      \* `images_dir` - directory for images. If `allow_convert_data` is True, the directory will be used for saving converted images, otherwise used for data reading. (Optional, can be not provided in conversion case, default value `<data_dir>/converted/images`).
      \* `depth_map_dir` - directory for reference depth maps, stored in numpy format. If `allow_convert_data` is True, the directory will be used for saving converted depth maps, otherwise used for data reading.
        (Optional, can be not provided in conversion case, default value `<data_dir>/converted/depth`). Please, note, you need to specify path to directory with depth maps with `additional_data_source` parameter in your config during evaluation.
      \* `allow_convert_data` - allows to convert data from HDF5 format (Optional, default False).
    \* `inpainting` - converts images to `ImageInpaintingAnnotation`.
      \* `images_dir` - path to images directory.
      \* `masks_dir` - path to mask dataset to be used for inpainting (Optional).
    \* `aflw2000_3d` - converts [AFLW2000-3D](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm) dataset for 3d facial landmarks regression task to `FacialLandmarks3DAnnotation`.
       \* `data_dir` - directory, where input images and annotation files in MATLAB format stored.
    \* `style_transfer` - converts images to `StyleTransferAnnotation`.
      \* `images_dir` - path to images directory.
      \* `annotation_file` - path to file with annotation in format: `[input_img1] [ref_img]` if you have single input model or `[input_img1] [input_img2] [ref_img]` if you have 2 inputs.
    \* `ade20k` - converts ADE20K dataset to `SegmentationAnnotation`.
      \* `images_dir` - path to directory with images (e.g. `ADEChallengeData2016/images/validation`).
      \* `annotations_dir` - path to directory with annotations (e.g. `ADEChallengeData2016/annotations/validation`).
      \* `object_categories_file` - path to file with labels (e.g. `ADEChallengeData2016/objectInfo150.txt`).
      \* `num_classes` - number of used classes.
    \* `criteo_kaggle_dac` - converts Criteo datasets to `ClassificationAnnotation`.
      \* `testing_file` - path to preprocessed Criteo file (e.g. `criteo/terabyte/terabyte_preprocessed,npz`).
      \* `binary` - Input file mode flag. If set, input file is in binary mode instead of .npz mode. Optional, default `False`
      \* `batch` - batch size expected by model
      \* `max_ind_range` - maximum index range for categorical features. Optional, default `0`
      \* `subsample_size` - number of batches in test-only dataset, If provided, total number of records is batch \* subsample_size
      \* `validation` - if provided, only second half of dataset converted to annotations, according to dataset definition
      \* `preprocessed_dir` - path to store preprocessed batch files (e.g. `criteo/terabyte/preprocessed`).
      \* `separator` - symbol used to separate feature identifiers from batch data filename.
      \* `dense_features` - name of model dense features input. Optional, default `input.1`
      \* `sparse_features` - name of model sparse features input. For multiple inputs use comma-separated list in form [name]:[index]. Optional, default `lS_i`
      \* `lso_features` - name of model offsets features input. Optional, default `lS_o`
      \* `save_preprocessed_features` - allow saving preprocessed input features into `preprocessed_dir` (Optional, default True).
    \* `features_regression` - converts dataset stored in a format of directories with preprocessed input numeric data (features) in text files and reference data in the same format to `FeatureRegressionAnnotation`.
     This approach allows comparing output of model from different frameworks (e.g. OpenVINO converted model and source framework realisation).
      \* `input_dir` - directory with input data files.
      \* `reference_dir` - directory with reference data. \*\*Note: inside converted annotation, path to directory is not stored, only file name, please use `additional_data_source` for providing prefix.\*\*
      \* `input_suffix` - suffix for input files (usually file extension). Optional, default `.txt`.
      \* `reference_suffix` - suffix for reference files (usually file extension). Optional, default `.txt`.
      \* `use_bin_data` - this flag specifies that input data in binary format, optional, default `False`
      \* `bin_data_dtype` - data type for reading binary data.
    \* `multi_feature_regression` - converts dataset stored in a format of directories with preprocessed input numeric data (features) in dictionary format, where keys are layer names and values - features and reference data in the same format to `FeatureRegressionAnnotation`.
     This approach allows comparing output of model from different frameworks (e.g. OpenVINO converted model and source framework realisation). Please note, that input and reference should be stored as dict-like objects in npy files.
      \* `data_dir` - directory with input and reference files.
       \* `input_suffix` - suffix for input files (usually file extension). Optional, default `in.npy`.
       \* `reference_suffix` - suffix for reference files (usually file extension). Optional, default `out.npy`.
       \* `prefix` - prefix for input files selection (Optional, ignored if not provided).
    \* `librispeech` - converts [librispeech](http://www.openslr.org/12) dataset to `CharachterRecognitionAnnotation`.
      \* `data_dir` - path to dataset directory, which contains converted wav files.
      \* `annotation_file` - path to file which describe the data which should be used in evaluation (`audio_filepath`, `text`, `duration`). Optional, used only for data filtering and sorting audio samples by duration.
      \* `use_numpy` - allows using preprocessed data stored in npy-files instead of audio (Optional, default False).
      \* `top_n` - numeric value for getting only the n-shortest samples \*\*Note:\*\* applicable only with `annotation_file` providing.
      \* `max_duration` - maximum clip duration to include into annotation. Default 0, means no duration checking.
    \* `criteo` - converts [Criteo](http://labs.criteo.com/2013/12/download-terabyte-click-logs/) datasets to `ClassificationAnnotation`.
      \* `testing_file` - Path to testing file, terabyte_preprocessed.npz (Criteo Terabyte) or day_6_processed.npz (Criteo Kaggle Dac)
      \* `batch` - Model batch.
      \* `subsample_size` - Subsample size in batches
      \* `validation` - Allows to use half of dataset for validation purposes
      \* `block` - Make batch-oriented annotations
      \* `separator` - Separator between input identifier and file identifier
      \* `preprocessed_dir` - Preprocessed dataset location
      \* `dense_features` - Name of model dense features input
      \* `sparse_features` - Name of model sparse features input. For multiple inputs use comma-separated list in form `[name]:[index]`
      \* `lso_features` - Name of lS_o-like features input
    \* `im2latex_formula_recognition` - converts im2latex-like datasets to `CharacterRecognitionAnnotation`. [Example of the dataset](http://lstm.seas.harvard.edu/latex/data/)
      \* `images_dir` - path to input images (rendered or scanned formulas)
      \* `formula_file` - path to file containing one formula per line
      \* `split_file` - path to file containing `img_name` and corresponding formula `index` in `formula_file` separated by tab per line
      \* `vocab_file` - file containing vocabulary to cast token class indices into human-readable tokens
    \* `dna_sequence` - converts dataset for DNA sequencing to `DNASequenceAnnotation`.
      \* `chunks_file` - npy file with input chunks.
      \* `ref_file` - npy file with reference sequence.
      \* `num_chunks` - subset size for usage in validation, if not provided the whole dataset will be used.
      \* `alphabet` - alphabet for sequence decoding (Optional, default ["N", "A", "C", "G", "T"]).
    \* `place_recognition` - converts dataset for image based localization task to `PlaceRecognitionAnnotation`
      \* `subset_file` - matlab file contains info about subset used in validation.
    \* `mpii` - converts MPII Human Pose Estimation dataset to `PoseEstimationAnnotation`.
      \* `annotation_file` - json-file with annotation.
      \* `headboxes_file` - numpy file with boxes contained head coordinates for each image.
      \* `gt_pos_file` - numpy file with ground truth keypoints, optional, if not provided, default keypoints from annotation will be used.
      \* `joints_visibility_file` - numpy file with ground truth keypoints visibility level, optional, if not provided, default visibility level from annotation will be used.
    \* `cluttered_mnist` - converts MNIST dataset from spatial transformer network [example](https://github.com/oarriaga/STN.keras/tree/master/datasets) to `ClassificationAnnotation`.
      \* `data_file` - npz file with the dataset.
      \* `split` - dataset split: `train` - for training subset, `valid` - for train-validation subset, `test` - for testing subset (Optional, default test).
      \* `convert_images` - allows convert images from raw data stored in npz and save them into provided directory (Optional, default True).
      \* `images_dir` - directory for saving converted images (Optional, if not provided, the images will be saved into converted_images directory in the same location, where data_file is stored)
    \* `antispoofing` - converts dataset for antispoofing classification task to `ClassificationAnnotation`
      \* `data_dir` - path to root folder of the dataset
      \* `annotation_file` - path to json file containing annotations to the dataset ({index: {path:"...", labels:[...], bbox:[...] (optional), ...})
      \* `label_id` - number of label in the annotation file representing spoof/real labels
      \* `dataset_meta_file` - path to json file with dataset meta (e.g. label_map)
    \* `sound_classification` - converts dataset for sound classification to `ClassificationAnnotation`. The dataset should be represented by directory with input wav files and annotation in 2 column csv format, where first column is audio file name and second is label id from dataset.
      \* `annotation_file` - csv file with the selected subset for evaluation, file structure described above.
      \* `audio_dir` - directory with input data, (optional, required only if you want check file existence during annotation conversion).
    \* `ade20k_image_translation` - converts ADE20K dataset to `ImageProcessingAnnotation` according to `reference_file`.
      \* `annotations_dir` - path to directory with annotations (e.g. `ADEChallengeData2016/annotations`).
      \* `reference_file` - path to file with pairs key (validation): value (train).
    \* `salient_object_detection` - converts dataset for salient object detection to `SalientRegionAnnotation`. The dataset should have following structure:
      1. images have numeric ids like names and `jpg` extension (e.g. image/0.jpg, image/1.jpg, image/2.jpg, ...).
      2. salience map located in separated directory, have the same ids like images and `png` extension  (e.g. mask/0.png, mask/1.png, mask/2.png).
      \* `images_dir` - directory with input images.
      \* `masks_dir` - directory with reference salience maps.
      \* `annotation_file` - txt file with selected image ids.
    \* `wflw` - converts WFLW dataset for facial landmarks regression task to `FacialLandmarksAnnotation`.
      \* `annotation_file` - path to txt file with ground truth data in WFLW dataset format.
      \* `images_dir` - path to dataset images, used only for content existence check (optional parameter).
    \* `common_object_detection` - converts object detection dataset to `DetectionAnnotation`. Dataset should be stored in following format:
      1. labels_map defined as text file, where defined labels line by line.
      2. annotations for each image stored in separated text file. Box is represented by space separated info: <label_id> <x_min> <y_min> <x_max> <y_max>.
      3. name of annotation file the same as image name (or additional file with file mapping should be defined).
      \* `annotation_dir` - path to directory with annotation files.
      \* `images_dir` - path to directory with images (Optional, used only for content check step).
      \* `labels_file` - path to file with labels.
      \* `pairs_file` - path to file where described image and annotation file pairs (Optional, if not provided list will be created according to annotation_dir content).
      \* `has_background` - flag that background label should be added to label_map (Optional, default False).
      \* `add_background_to_label_id` - flag that label_ids defined in annotation should be shifted if `has_background` enabled.
    \* `see_in_the_dark` - converts See-in-the-Dark dataset described in the [paper](https://cchen156.github.io/paper/18CVPR_SID.pdf) to `ImageProcessingAnnotation`.
      \* `annotation_file` - path to image pairs file in txt format.
    \* `conll_ner` - converts CONLL 2003 dataset for Named Entity Recognition to `BERTNamedEntityRecognitionAnnotation`.
      \* `annotation_file` - annotation file in txt format.
      \* `vocab_file` - vocab file for word piece tokenization.
      \* `lower_case` - converts all tokens to lower case during tokenization (Optional, default `False`).
      \* `max_length` - maximal input sequence length (Optional, default 128).
      \* `pad_input` - allow padding for input sequence if input less that `max_length` (Optional, default `True`).
      \* `include_special_token_lables` - allow extension original dataset labels with special token labels (`[CLS'`, `[SEP]`]) (Optional, default `False`).
      \* `labels_file` - path to file with custom labels in json format (Optional).
      Example of labels_file content:
        ```json
        {"labels": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]}

• tacotron2_data_converter - converts input data for custom tacotron2 pipeline.

  • annotation_file - tsv file with location input data and reference.

• noise_suppression_dataset - converts dataset for audio denoising to NoiseSuppressionAnnotation

  • annotation_file - txt file with file pairs <clean_signal> <noisy_signal>.

• vimeo90k - converts Vimeo-90K dataset for a systematic evaluation of video processing algorithms to SuperResolutionAnnotation.

  • annotation_file - path to text file with list of dataset setuplets included in test.

  • add_flow - allows annotation of flow data (optional, default False).

• vimeo90k_interp - converts Vimeo-90K intermediate frame interpolation dataset for a systematic evaluation of video processing algorithms to ImageProcessingAnnotation.

  • annotation_file - path to text file with list of dataset setuplets included in test.

• kaldi_asr_data - converts preprocessed Kaldi* features dataset to CharacterRecognitionAnnotation.

  • annotation_file - file with gt transcription table.

  • data_dir - directory with ark files.

  • features_subset_file - file with list testing ark files, Optional, if not provided, all found in data_dir files will be used.

  • ivectors - include ivectors features to input, Optional, default False.

• kaldi_feat_regression - converts preprocessed Kaldi* features to RegressionAnnotation.

  • data_dir - directory with input ark or npz files.

  • features_subset_file - file with list testing ark or npz files, Optional, if not provided, all found in data_dir files will be used. Subset file has following format: each line stores information about input sample. Input sample consists of space separated list of input files and ends with reference file name.

  • ivectors - include ivectors features to input, Optional, default False.

  • ref_data_dir - directory with reference ark or npz files (Optional, if not provided data_dir will be used instead).

  • vectors_mode - allow usage each vector in utterance as independent data.

  • ref_file_suffix - suffix for search reference files (Optional, default _kaldi_score).

  • utterance_name_agnostic - allow ignoring utterance name during conversion. Optional, default required utterance keys matching.

  • use_numpy_data - allow using data stored in npz instead ark files as preferable data format.

• electricity - converts Electricity dataset to TimeSeriesForecastingAnnotation.

  • data_path_file - Path to dataset file in .csv format.

  • num_encoder_steps - The maximum number of historical timestamps that model use.

• yolo_labeling - converts object detection dataset with annotation in YOLO labeling format to DetectionAnnotation.

  • annotations_dir - path to directory with annotation files in txt format.

  • images_dir - path to directory with images (Optional).

  • labels_file - path to file with labels in txt format (Optional).

  • images_suffix - suffix for image file names (Optional, default: .jpg).

• label_me_detection - converts dataset obtained using LabelMe Annotation Tool to DetectionAnnotation.

  • annotations_dir - path to directory with annotation files in xml format.

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding). More details in Customizing dataset meta section.

  • images_dir - path to directory with images (Optional).

  • has_background - allows convert dataset with/without adding background_label (Optional, default: False).

• label_me_segmentation - converts dataset obtained using LabelMe Annotation Tool to SegmentationAnnotation.

  • annotations_dir - path to directory with annotation files in xml format.

  • dataset_meta_file - path to json file with dataset meta (e.g. label_map, color_encoding). More details in Customizing dataset meta section.

  • images_dir - path to directory with images (Optional).

  • masks_dir - path to directory with ground truth segmentation masks (Optional).

• cls_dataset_folder - converts generic classification dataset with DatasetFolder format to ClassificationAnnotation.

  • data_dir - directory with input images in following structure:

    data_dir/class_a/xxx.ext
    data_dir/class_a/xxy.ext
    data_dir/class_b/[...]/xxz.ext
    ...
    data_dir/class_y/123.ext
    data_dir/class_z/nsdf3.ext
    data_dir/class_z/[...]/asd932_.ext

• rctw_preprocessed - converts preprocessed Reading Chinese Text in the Wild (RCTW) dataset to TextDetectionAnnotation.

  • annotation_file - txt file contains annotation, where image and its annotation separated by tab. Image annotation is represented as json-string.

• open_images_detection - converts Open Images dataset for object detection task to DetectionAnnotation.

  • bbox_csv_file - path to cvs file which contains bounding box coordinates.

  • labels_file - path to file with class labels in csv format.

  • images_dir - path to images folder (Optional).

  • label_start - specifies label index start in label map. You can provide another value, if you want to use this dataset for separate label validation (Optional, default value is 1).

• amazon_product_data - converts book reviews from Amazon Product Data (http://snap.stanford.edu/data/amazon/productGraph) dataset preprocessed for DIEN model to ClassificationAnnotation.

  • test_data - path to file with user reviews

  • uid_voc - path to user identification vocabulary

  • mid_voc - path to movies identification vocabulary

  • cat_voc - path to category vocabulary

  • item_info - path to item-info file

  • reviews_info - path to reviews-info file

  • preprocessed_dir - path to preprocessed data

  • separator - separator between input name and batch identifier in preprocessed data filename. (Optional, default #)

  • mid_his_batch - identifier of model mid_his_batch input

  • cat_his_batch - identifier of model cat_his_batch input

  • uid_batch - identifier of model uid_batch input

  • mid_batch - identifier of model mid_batch input

  • cat_batch - identifier of model cat_batch input

  • mask - identifier of model mask input

  • seq_len - identifier of model seq_len input

  • skip_dump - allow to skip storing preprocessed data. (Optional, default: False)

  • batch - batch for data preprocessing. (Optional, default 1)

  • max_len - maximum input sequence length. (Optional, default 100)

  • subsample_size - limit number of preprocessed sentences. (Optional, default 0, no limitation)

• wgs_tf_records - converts Deepvariant WGS preprocessed dataset (https://github.com/google/deepvariant/blob/r0.10/docs/deepvariant-training-case-study.md) to ClassificationAnnotation.

  • annotation_file - path to Deepvariant WGS preprocessed dataset file

  • preprocessed_dir - path to preprocessed data

  • skip_dump - allow to skip storing preprocessed data. (Optional, default: False)

• wgs_pickle_records - converts Deepvariant WGS preprocessed dataset (https://github.com/google/deepvariant/blob/r0.10/docs/deepvariant-training-case-study.md) dumped to pickle file to ClassificationAnnotation.

  • annotation_file - path to Deepvariant WGS preprocessed dataset file

  • preprocessed_dir - path to preprocessed data

  • skip_dump - allow to skip storing preprocessed data. (Optional, default: False)

• common_text_recognition - converts annotation for text recognition tasks (e.g. optical character recognition, automatic speech recognition) to CharacterRecognitionAnnotation.

  • annotation_file - path to file with annotation, where each line represented as [input_file] [reference].

• speaker_reidentification - converts annotation for speaker/sound reidentification dataset to ReidentificationClassificationAnnotation.

  • annotation_file - text file containing reidentification pairs in following format: [is_positive] [audio_1] [audio_2], where is_positive - indicator that pair is positive (1 if speaker is the same, 0 if speakers are different), audio_1 and audio_2 are path to the first and the second audio files respectively.

  • max_pairs - limit for selection pairs for each image (Optional, by default use all founded pairs in annotation).

• mvtec - converts MVTec dataset to AnomalySegmentationAnnotation or ClassificationAnnotation.

  • data_dir - directory with subset images.

  • classification_only - converts dataset to ClassificationAnnotation withot saving pixel level information (Optional, default False).

• kitti_2d_detection - converts KITTI annotation for 2D object detection task to DetectionAnnotation.

  • annotations_dir - path to directory with annotation files.

  • labels_file - path to file with labels.

  • images_dir - path to directory with images (optional, default image_2).

  • label_start - specifies label index start in label map. Optional, default value is 1. You can provide another value, if you want to use this dataset for separate label validation.

  • images_suffix - suffix for image file names (Optional, default: .png).

• malware_classification_converter - converts dataset for malware detection task to ClassificationAnnotation.

  • annotation_file - path to dataset annotation file.

  • data_dir - path to dataset root folder.

  • known_folder - name of folder containing known files (optional, default KNOWN_1000).

  • malicious_folder - name of folder containing malicious files (optional, default MALICIOUS_1000).

• cvat_palm_detection - converts Hands dataset for palm detection task to DetectionAnnotation.

  • annotation_file - path to dataset annotation file.

  • images_dir - path to directory with images (optional, default final).

  • landmarks_file - path to file with hand landmarks coordinates for annotation images (optional). If provided, converter uses GT box coordinates calculated from landmarks data instead of data provided in annotation_file.

  • padding - additional padding, in pixels, around GT bounding box, calculated from landmarks_file.

• cvat_hand_landmarks - converts Hands dataset for hand recognition task to HandLandmarksAnnotation.

  • annotation_file - path to dataset annotation file.

  • bbox_file - path to file with hand bounding box annotation.

  • images_dir - path to directory with images (optional, default final).

  • from_landmarks - allow to calculate hand bounding box coordinates from landmarks data instead of data provided in bbox_file (optional, default False).

  • padding - additional padding, in pixels, around hand bounding box, calculated in from_landmarks mode (optional, default 10).

  • num_keypoints - number of keypoints in annotation expected by model (optional, default 21).

• parti_prompts - convert PartiPrompts for text to image generation benchmarking to Text2ImageGenerationAnnotation.

  • annotation_file - path to annotation file in csv format.

Customizing Dataset Meta

There are situations when we need to customize some default dataset parameters (e.g. replace original dataset label map with own.) You are able to overload parameters such as label_map, segmentation_colors, background_label using dataset_meta_file argument. Dataset meta file is JSON file, which can contain the following parameters:

• label_map is the dictionary where <CLASS_ID> is key and <CLASS_NAME> - value.

• labels is the list of strings, which represent class names (order is matter, the index of class name used as class id). Can be used instead label_map.

• background_label - id of background label in the dataset.

• segmentation_colors (if your dataset for semantic segmentation task uses color encoding). Segmentation colors is a list of channel-wise values for each class. (e.g. if your dataset has 3 classes in BGR colors, segmentation colors for it will looks like: [[255, 0, 0], [0, 255, 0], [0, 0, 255]]). Example of dataset_meta.json content:

  {
  "label_map": {"0": "background", "1": "cat", "2": "dog"},
  "background_label": "0",
  "segmentation_colors": [[0, 0, 0], [255, 0, 0], [0, 0, 255]]
  }