Dataset

A Dataset combines together images and annotations to provide quick functionalities to manage the data.

Use a Dataset to:

query images and annotations in remo
annotate
export annotations
feed data to a training model
upload model predictions

class remo.Dataset¶

Remo dataset

documentation

class remo.Dataset(id: int = None, name: str = None, quantity: int = 0, \*\*kwargs)

Parameters
- id – dataset id
- name – dataset name
- quantity – number of images

add_annotations¶

Fast upload of annotations to the Dataset.

If annotation_set_id is not provided, annotations will be added to:

the only annotation set present, if the Dataset has exactly one Annotation Set and the tasks match
a new annotation set, if the Dataset doesn’t have any Annotation Sets or if create_new_annotation_set = True

Otherwise, annotations will be added to the Annotation Set specified by annotation_set_id.

Example::

urls = [‘[https://remo-scripts.s3-eu-west-1.amazonaws.com/open_images_sample_dataset.zip](https://remo-scripts.s3-eu-west-1.amazonaws.com/open_images_sample_dataset.zip)’]
my_dataset = remo.create_dataset(name = ‘D1’, urls = urls)
image_name = ‘000a1249af2bc5f0.jpg’
annotations = []

annotation = remo.Annotation()
annotation.img_filename = image_name
annotation.classes=’Human hand’
annotation.bbox=[227, 284, 678, 674]
annotations.append(annotation)

annotation = remo.Annotation()
annotation.img_filename = image_name
annotation.classes=’Fashion accessory’
annotation.bbox=[496, 322, 544,370]
annotations.append(annotation)

my_dataset.add_annotations(annotations)

documentation

add_annotations(annotations: List[remo.domain.annotation.Annotation], annotation_set_id: int = None, create_new_annotation_set: bool = False)

Parameters
- annotations – list of Annotation objects
- annotation_set_id – annotation set id
- create_new_annotation_set – if True, a new annotation set will be created

add_data¶

Adds images and/or annotations to the dataset.

Use the parameters as follows:

Use local files to link (rather than copy) images.
Use paths_to_upload if you want to copy image files or archive files.
Use urls to download from the web images, annotations or archives.

In terms of supported formats:

Adding images: support for jpg, jpeg, png, tif
Adding annotations: to add annotations, you need to specify the annotation task and make sure the specific file format is one of those supported. See documentation here: https://remo.ai/docs/annotation-formats/
Adding archive files: support for zip, tar, gzip

Example::

! wget ‘[https://s-3.s3-eu-west-1.amazonaws.com/open-images.zip](https://s-3.s3-eu-west-1.amazonaws.com/open-images.zip)’
! unzip open-images.zip

urls = [‘[https://s-3.s3-eu-west-1.amazonaws.com/open-images.zip](https://s-3.s3-eu-west-1.amazonaws.com/open-images.zip)’]
my_dataset = remo.create_dataset(name = ‘D1’)
my_dataset.add_data(local_files=[‘./open-images’], annotation_task = ‘Object detection’)

documentation

add_data(local_files: List[str] = None, paths_to_upload: List[str] = None, urls: List[str] = None, annotation_task: str = None, folder_id: int = None, annotation_set_id: int = None, class_encoding=None, wait_for_complete=True)

Parameters
- dataset_id – id of the dataset to add data to
- local_files – list of files or directories containing annotations and image files Remo will create smaller copies of your images for quick previews but it will point at the original files to show original resolutions images. Folders will be recursively scanned for image files.
- paths_to_upload – list of files or directories containing images, annotations and archives. These files will be copied inside .remo folder. Folders will be recursively scanned for image files. Unpacked archive will be scanned for images, annotations and nested archives.
- urls – list of urls pointing to downloadable target, which can be image, annotation file or archive.
- annotation_task – annotation tasks tell remo how to parse annotations. See also: remo.task.
- folder_id – specifies target virtual folder in the remo dataset. If None, it adds to the root level.
- annotation_set_id – specifies target annotation set in the dataset. If None, it adds to the default annotation set.
- class_encoding – specifies how to convert labels in annotation files to readable labels. If None, Remo will try to interpret the encoding automatically - which for standard words, means they will be read as they are. See also: remo.class_encodings.
- wait_for_complete – if True, the function waits for upload data to complete

Returns

Dictionary with results for linking files, upload files and upload urls:

{
    'files_link_result': ...,
    'files_upload_result': ...,
    'urls_upload_result': ...
}

annotation_sets¶

Lists the annotation sets within the dataset.

documentation

annotation_sets()

Returns

List[remo.AnnotationSet]

annotations¶

Returns all annotations for a given annotation set. If no annotation set is specified, the default annotation set will be used

documentation

annotations(annotation_set_id: int = None)

Parameters

annotation_set_id – annotation set id
Returns

List[remo.Annotation]

classes¶

Lists all the classes within the dataset

documentation

classes(annotation_set_id: int = None)

Parameters

annotation_set_id – annotation set id. If not specified the default annotation set is considered.
Returns

List of classes

create_annotation_set¶

Creates a new annotation set within the dataset If paths_to_files is provided, it populates it with the given annotations. The first created annotation set for the given dataset, is considered the default one.

documentation

create_annotation_set(annotation_task: str, name: str, classes: List[str] = [], paths_to_files: List[str] = None)

Parameters
- annotation_task – annotation task. See also: remo.task
- name – annotation set name
- classes – list of classes to prepopulate the annotation set. Example: [‘Cat’, ‘Dog’]. Default is no classes
- paths_to_files – list of paths to files or directories containing files to be uploaded. Useful to upload annotatations while creating an annotation set. Default: None
Returns

remo.AnnotationSet

default_annotation_set¶

If the dataset has only one annotation set, it returns that annotation set. Otherwise, it raises an exception.

documentation

default_annotation_set()

delete¶

Deletes dataset

documentation

delete()

export_annotations_to_file¶

Exports annotations for a given annotation set in a given format and saves it to a file. If export_tags = True, output_file needs to be a .zip file.

It offers some convenient export options, including:

Methods to append the full_path to image filenames,
Choose between coordinates in pixels or percentages,
Export tags to a separate file
Export annotations filtered by user-determined tags.

Example::

# Download and unzip this sample dataset: [https://s-3.s3-eu-west-1.amazonaws.com/dogs_dataset.json](https://s-3.s3-eu-west-1.amazonaws.com/dogs_dataset.json)
dogs_dataset = remo.create_dataset(name = ‘dogs_dataset’,

> local_files = [‘dogs_dataset.json’],
> annotation_task = ‘Instance Segmentation’)

dogs_dataset.export_annotations_to_file(output_file = ‘./dogs_dataset_train.json’,

    annotation_format = ‘coco’,
    append_path = False,
    export_tags = False,
    filter_by_tags = ‘train’)

documentation

export_annotations_to_file(output_file: str, annotation_set_id: int = None, annotation_format: str = 'json', export_coordinates: str = 'pixel', append_path: bool = True, export_tags: bool = True, filter_by_tags: list = None)

Parameters
- output_file – output file to save. Includes file extension and can include file path. If export_tags = True, output_file needs to be a .zip file
- annotation_set_id – annotation set id
- annotation_format – can be one of [‘json’, ‘coco’, ‘csv’]. Default: ‘json’
- append_path – if True, it appends the image path to the filename, otherwise it uses just the filename. Default: True
- export_coordinates – converts output values to percentage or pixels, can be one of [‘pixel’, ‘percent’]. Default: ‘pixel’
- export_tags – if True, it also exports tags to a separate CSV file. Default: True
- filter_by_tags – allows to export annotations only for images containing certain image tags. It can be of type List[str] or str. Default: None

fetch¶

Updates dataset information from server

documentation

fetch()

get_annotation_set¶

Retrieves annotation set with given id. If no annotation set id is passed:

if the dataset has only one annotation set, it returns that one

if the dataset has multiple annotation sets, it raises an error

documentation

get_annotation_set(annotation_set_id: int = None)

Parameters

annotation_set_id – annotation set id
Returns

remo.AnnotationSet

get_annotation_statistics¶

Retrieves annotation statistics of a given annotation set. If annotation_set_id is not provided, it retrieves the statistics of all the available annotation sets within the dataset.

documentation

get_annotation_statistics(annotation_set_id: int = None)

Returns

list of dictionaries with fields annotation set id, name, num of images, num of classes, num of objects, top3 classes, release and update dates

image¶

Returns the remo.Image with matching img_filename or img_id. Pass either img_filename or img_id.

documentation

image(img_filename=None, img_id=None)

Parameters
- img_filename – filename of the Image to retrieve
- img_id – id of the the Image to retrieve
Returns

remo.Image

images¶

Lists images within the dataset

documentation

images(limit: int = None, offset: int = None)

Parameters
- limit – the number of images to be listed
- offset – specifies offset
Returns

List[remo.Image]

Example::

my_dataset.images()

info¶

Prints basic info about the dataset:

Dataset name
Dataset ID
Number of images contained in the dataset
Number of annotation sets contained in the dataset

documentation

info()

list_image_annotations¶

Retrieves annotations for a given image

documentation

list_image_annotations(annotation_set_id: int, image_id: int)

Parameters
- annotation_set_id – annotation set id
- image_id – image id
Returns

List[remo.Annotation]

search_images¶

Search images by filename, classes and tags

Examples::

my_dataset.search_images(classes = [“dog”,”person”])
my_dataset.search_images(image_name_contains = “pic2”)

documentation

search_images(annotation_sets_id: int = None, classes: str = None, classes_not: str = None, tags: str = None, tags_not: str = None, image_name_contains: str = None, limit: int = None)

Parameters
- annotation_sets_id – the annotation sets ID to search into (can be multiple, e.g. [1, 2]). No need to specify it if the dataset has only one annotation set
- classes – string or list of strings - search for images which have objects of all the given classes
- classes_not – string or list of strings - search for images excluding those that have objects of all the given classes
- tags – string or list of strings - search for images having all the given tags
- tags_not – string or list of strings - search for images excluding those that have all the given tags
- image_name_contains – search for images whose name contains the given string
- limit – limits number of search results (by default returns all results)
Returns

List[remo.AnnotatedImage]

view¶

Opens browser on dataset page

documentation

view()

view_annotate¶

Opens browser on the annotation tool for the given annotation set

documentation

view_annotate(annotation_set_id: int = None)

Parameters

annotation_set_id – annotation set id. If the dataset has only one annotation set, there is no need to specify the annotation_set_id.

view_annotation_stats¶

Opens browser on annotation set insights page

documentation

view_annotation_stats(annotation_set_id: int = None)

Parameters

annotation_set_id – annotation set id. If the dataset has only one annotation set, there is no need to specify the annotation_set_id.

view_image¶

Opens browser on image view page for the given image

documentation

view_image(image_id: int)

Parameters

image_id – image id