First steps with Remo python library

Create and visualize a dataset

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

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'

You can read more about what type of annotation tasks and formats we support in our documentation.

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:

  • Manipulating annotation sets from code
  • Custom uploading of annotations, predictions and images
  • Advanced images search
  • Organising data in virtual folders