Skip to main content

lours logo

The ultimate companion library for object detection data

| 📚 Docs| 📊 Slides | 📝 Blogpost (coming soon) |

codecov GitHub License PyPI - Python Version PyPI - Version PyPI - Downloads Read the Docs

Why Lours ?

The overall complexity and lack of standardization in public object detection data makes it painstakingly hard to work with. Lours is a library that makes construction, conversion and manipulation of any detection dataset easy, fast and reliable.

lours logo

The Dataset class

At its core, Lours features a unified Dataset class. It offers :

  • Seamless conversion between popular formats (COCO, PascalVOC, FiftyOne, ...)
  • Powerful dataset operations (merging, smart train/val splitting, class remapping, bbox format conversion...)
  • Image and Object-Level indexing
  • ... and much more !

A Lours dataset object is essentially composed of two pandas.DataFrame :

  • images containing image-level data
  • annotations containing object-instance-level data
from lours.dataset import Dataset, from_coco

COCO_dataset = from_coco("docs/notebooks/notebook_data/coco_valid.json")
COCO_dataset

image_df annot_df

In addition to Lours-specific utilities and functions, most operations supported by pandas DataFrames can also be applied seamlessly to Lours datasets.

Check the Dataset 101 Tutorial or the full API Reference for more details about the Dataset class.

Evaluators

Lours also features evaluators to compare a set of predictions to a ground truth in several settings :

  • DetectionEvaluator
  • CrowdDetectorEvaluator

Check the Object Detection Evaluation Tutorial of the API Reference for more details.

Installation

Simply run :

pip install lours

You can also install the pre-release by adding the --pre option

pip install lours --pre

From source

  • pip
git clone https://github.com/XXII-AI/Lours.git
cd Lours
pip install -e . # Only for pip > 21.3
  • poetry >= 1.2

Assuming both lours and your project are in the same folder, adapt the relative path of the repo if needed

poetry add --editable ../lours/
poetry add --editable https://github.com/XXII-AI/lours.git

Tutorials

A list of tutorials is available in the Lours documentation tutorials.

We also provide examples notebooks in the docs/notebooks folder.

Documentation

For details about the Lours API, see the reference documentation :

Metadata

Release files for lours 1.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for lours 1.3.0
File Size Uploaded
lours-1.3.0.tar.gz 143.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for lours 1.3.0
File Interpreter ABI Platform
lours-1.3.0-py3-none-any.whl Python 3 none any Details

Total release size: 309.7 kB

Release files / lours-1.3.0.tar.gz

Download URL lours-1.3.0.tar.gz
Size 143.2 kB
Tags Source
SHA-256 checksum
How to use checksums
2e0f82aab05311549918a595b32f163bcc5054f7016cb262765eb8c79d6f76cc
BLAKE2b-256 checksum
How to use checksums
6bf8cb37192ef58bda61c4dafda9de377390160118dff86479b6653ff653e7b9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 18, 2026.

Transparency log

Release files / lours-1.3.0-py3-none-any.whl

Download URL lours-1.3.0-py3-none-any.whl
Size 166.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a4168840ca17ecfae92de2f6b41fbaa0a8e5fc41cfbec6adb1945cb5d68713ee
BLAKE2b-256 checksum
How to use checksums
e697077db89dc53043e810fe53a3f39d7c3f7c01759c2682010780ee1fe2244b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 18, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

1.3.0 This release

2 release files

1.2.1

2 release files

1.2.0

2 release files

1.1.0

2 release files

1.0.1

2 release files

1.0.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page