First steps with Remo python library

Create and visualize a dataset

Let's create a new dataset and upload some annotations.

Read more about what type of annotation tasks and formats we support in our documentation.

import remo
import pandas as pd
    (>':') Remo server is running: {'app': 'remo', 'version': '0.3.5-455-g887e55a2'}
urls = ['']

remo.create_dataset(name = 'open images detection',
                    urls = urls,
                    annotation_task = "Object detection")
{'files uploaded': 10, 'annotations': 10, 'errors': []}

Dataset 14 - 'open images detection'

We can easily list all datasets and retrieve one

[Dataset 2 - 'OCR_symbols',
 Dataset 6 - 'cars_detection',
 Dataset 14 - 'open images detection']
# make sure to use the right ID when running the tutorial
my_dataset = remo.get_dataset(14)
Open http://localhost:8123/datasets/14


Visualize Annotation Statistics

To explore annotations, we can print the stats of the annotation sets or open the interactive UI

[{'AnnotationSet ID': 10,
  'AnnotationSet name': 'Object detection',
  'n_images': 10,
  'n_classes': 18,
  'n_objects': 98,
  'top_3_classes': [{'name': 'Fruit', 'count': 27},
   {'name': 'Sports equipment', 'count': 12},
   {'name': 'Human arm', 'count': 10}],
  'creation_date': None,
  'last_modified_date': '2020-02-23T20:55:51.040660Z'}]
Open http://localhost:8123/annotation-detail/10/intro


Export Annotations

We can easily export annotations in a standardised format, and use them for training a model or further analysis

my_dataset.export_annotations_to_file('output.csv', annotation_format='csv')

Further SDK functionalities

Refer to the other tutorials and the documentation to explore further the SDK.

Other functionalities include:

  • Searching for images by class / tag
  • Manipulating annotation sets from code
  • Uploading futher annotations / images