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
urls = ['']

my_dataset = remo.create_dataset(name = 'open images detection',
                    urls = urls,
                    annotation_task = "Object detection")

Acquiring data - completed
Processing data - completed
Data upload completed

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

We can easily list all datasets and retrieve one


[Dataset 1 - 'ocr_symbols', Dataset 2 - 'test', Dataset 8 - 'open_images', Dataset 9 - 'test', Dataset 12 - 'open images detection']

# make sure to use the right ID when running the tutorial
new_dataset = remo.get_dataset(1)

Let's visualise our dataset



Visualize Annotation Statistics

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


[{'AnnotationSet ID': 41, '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': '2020-05-29T13:38:52.259776Z'}]



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