---
license: apache-2.0
task_categories:
- image-text-to-text
tags:
- image
language:
- en
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train/*
  - split: valid
    path: data/validation/*
  - split: test
    path: data/test/*
---

# Open Images V7 + Narratives

[Original Source](https://storage.googleapis.com/openimages/web/index.html) | [Google Localized Narrative](https://google.github.io/localized-narratives/)

## 📌 Introduction

This dataset collects the images and annotations from the original Open Images Dataset V7 and the annotations from the project [localized-narratives](https://github.com/google/localized-narratives).

Out of the 9M images, a subset of 1.9M images have been annotated with: bounding boxes, object segmentations, visual relationships, localized narratives, point-level labels, and image-level labels. (The remaining images have only image-level labels).

## 🙏 Acknowledgement

All credits to the original Open Images Dataset V7 and the localized-narratives teams.

## 📜 Cite

Please consider to cite the following related papers:

1. ["Extreme clicking for efficient object annotation"](https://arxiv.org/abs/1708.02750), Papadopolous et al., ICCV 2017.

2. ["We don't need no bounding-boxes: Training object class detectors using only human verification"](https://arxiv.org/abs/1602.08405), Papadopolous et al., CVPR 2016.

3. ["The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale"](https://arxiv.org/abs/1811.00982), Kuznetsova et al., arXiv:1811.00982 2018.

4. ["Large-scale interactive object segmentation with human annotators"](https://arxiv.org/pdf/1903.10830), Benenson et al., CVPR 2019.

5. ["Natural Vocabulary Emerges from Free-Form Annotations"](https://arxiv.org/abs/1906.01542), Pont-Tuset et al., arXiv 2019.

6. ["From couloring-in to pointillism: revisiting semantic segmentation supervision"](https://storage.googleapis.com/openimages/web_v7/2022_pointillism_arxiv.pdf), Benenson et al., arXiv 2022.