Skip to main content

Utilities for building and working with computer vision datasets

Project description



This repo contains utilities for building and working with computer vision datasets, developed by Xtract AI.

So far, APIs for the following open-source datasets are included:

  1. COCO 2017 (detection and segmentation): xt_cvdata.apis.COCO
  2. Open Images V5 (detection and segmentation): xt_cvdata.apis.OpenImages
  3. Visual Object Tagging Tool (VoTT) CSV output (detection): xt_cvdata.apis.VoTTCSV

More to come.


From PyPI:

pip install xt-cvdata

From source:

git clone
pip install ./xt-cvdata


See specific help on a dataset class using help. E.g., help(xt_cvdata.apis.COCO).

Building a dataset

from xt_cvdata.apis import COCO, OpenImages

# Build an object populated with the COCO image list, categories, and annotations
coco = COCO('/nasty/data/common/COCO_2017')

# Same for Open Images
oi = OpenImages('/nasty/data/common/open_images_v5')

# Get just the person classes
oi.subset(['Person']).rename({'Person': 'person'})

# Merge and build
merged = coco.merge(oi)'./data/new_dataset_dir')

This package follows pytorch chaining rules, meaning that methods operating on an object modify it in-place, but also return the modified object. The exception is the merge() method which does not modify in-place and returns a new merged object. Hence, the above operations can also be completed using:

from xt_cvdata.apis import COCO, OpenImages

merged = (
                .rename({'Person': 'person'})

In practice, somewhere between the two approaches will probably be most readable.

The current set of dataset operations are:

  • analyze: recalculate dataset statistics (e.g., class distributions, train/val split)
  • verify_schema: check if class attributes follow required schema
  • subset: remove all but a subset of classes from the dataset
  • rename: rename/combine dataset classes
  • sample: sample a specified number of images from the train and validation sets
  • split: define the proportion of data in the validation set
  • merge: merge two datasets together, returning merged dataset
  • build: create the currently defined dataset using either symlinks or by copying images

Implementing a new dataset type

New dataset types should inherit from the base xt_cvdata.Builder class. See the Builder, COCO and OpenImages classes as a guide. Specifically, the class initializer should define info, licenses, categories, annotations, and images attributes such that self.verify_schema() runs without error. This ensures that all of the methods defined in the Builder class will operate correctly on the inheriting class.

Data Sources

[descriptions and links to data]


[list of dependencies and their licenses, including data]


[list of references]

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for xt-cvdata, version 0.4.0
Filename, size File type Python version Upload date Hashes
Filename, size xt_cvdata-0.4.0-py3-none-any.whl (16.1 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size xt-cvdata-0.4.0.tar.gz (13.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page