README.md

---
language:
- en

license: other
license_name: open-images-cc-by
license_link: https://storage.googleapis.com/openimages/web/factsfigures_v7.html

pretty_name: Animals Detection Images Dataset

task_categories:
- object-detection

tags:
- object-detection
- animals
- wildlife
- open-images
- computer-vision
- ultralytics
- yolo

size_categories:
- 10K<n<100K

configs:
- config_name: default
  data_files:
  - split: train
    path:
    - data/images/train/shard_000/metadata.jsonl
    - data/images/train/shard_001/metadata.jsonl
    - data/images/train/shard_002/metadata.jsonl
    - data/images/train/shard_003/metadata.jsonl
    - data/images/train/shard_004/metadata.jsonl
    - data/images/train/shard_005/metadata.jsonl
    - data/images/train/shard_006/metadata.jsonl
  - split: validation
    path:
    - data/images/valid/shard_000/metadata.jsonl
    - data/images/valid/shard_001/metadata.jsonl
  - split: test
    path:
    - data/images/test/shard_000/metadata.jsonl
    - data/images/test/shard_001/metadata.jsonl
    - data/images/test/shard_002/metadata.jsonl
---

<h1 align="center">Animals Detection Images Dataset (Object Detection)</h1>

<p align="center">
  <img src="animalsdet_banner.jpg" alt="Animals Detection Dataset Banner"/>
</p>

![Task](https://img.shields.io/badge/Task-Object%20Detection-blue?style=flat-square)
![Dataset](https://img.shields.io/badge/Dataset-Animals%20Detection-0aa1a7?style=flat-square)
![Format](https://img.shields.io/badge/Format-YOLO%20Compatible-orange?style=flat-square)
![Classes](https://img.shields.io/badge/Classes-80-success?style=flat-square)
![Splits](https://img.shields.io/badge/Splits-Train%20%7C%20Val%20%7C%20Test-blueviolet?style=flat-square)
![License](https://img.shields.io/badge/License-Open%20Images%20CC%20BY-lightgrey?style=flat-square)

> **Unofficial redistribution of an 80-class wildlife/animal object-detection subset of Google Open Images, reformatted into a standardized YOLO-compatible layout with a seeded validation split.**

## Dataset Description

- **Homepage:** https://storage.googleapis.com/openimages/web/index.html
- **Paper:** https://doi.org/10.1007/s11263-020-01316-z
- **Repository:** https://github.com/EscVM/OIDv4_ToolKit

## Disclaimer

This repository is **not** an official release of Open Images.

The images and their original box annotations were collected and released by Google (the Open Images project); the underlying photographs themselves were taken by their individual Flickr photographers, who retain copyright subject to the Creative Commons terms they published under. This repository does **not** claim ownership of any images, annotations, or metadata.

**Two-hop provenance.** This redistribution's raw files came from a Kaggle re-export, ["Animals Detection Images Dataset"](https://www.kaggle.com/datasets/antoreepjana/animals-detection-images-dataset) by Kaggle user antoreepjana, who downloaded an 80-class animal subset of Open Images V6+ via the community [OIDv4_ToolKit](https://github.com/EscVM/OIDv4_ToolKit). That Kaggle page tags the dataset `CC0: Public Domain`, but this is **not accurate** for the underlying data -- see [License](#license) below for the real, verified terms (Open Images' own licensing page) and why they still permit this mirror.

---

# Dataset Overview

This dataset covers **80 animal classes** spanning mammals, birds, reptiles, fish, and invertebrates (e.g. `Lion`, `Zebra`, `Eagle`, `Jellyfish`, `Butterfly`, `Centipede`) -- the widest class vocabulary of any object-detection dataset DetectionBench has benchmarked. Boxes are large on average (median ~32% of image area) and images are typically single- or few-animal shots, though some contain many instances of one class (flocks, schools -- up to 123 boxes in one image).

---

# Changes from the Official Release

- **Cross-class duplicate images merged.** Because OIDv4 downloads per class query, the same physical photo is sometimes fetched into more than one class folder when it contains multiple labeled animals (confirmed on this release: a horse/mule photo is byte-identical across the `Horse` and `Mule` folders, each folder's label file holding only that folder's class). This repository de-duplicates by filename within each split and merges every class's boxes for that image into one entry, rather than treating each class-folder copy as a separate image. This reduced the raw file-copy counts (22,566 train-side, 6,505 test-side) down to the true unique-image counts below.
- **A validation split was carved out of train.** The source ships only `train`/`test`. This repository holds out a **seeded random 15%** of `train` (seed 42) as `valid`, keeping the official `test` split as-is.
- **Format converted.** OIDv4's per-image `<ClassName> <XMin> <YMin> <XMax> <YMax>` absolute-pixel `.txt` labels were converted to YOLO-normalized `.txt` labels and to canonical COCO JSON (`[x, y, w, h]`) per split.
- **Sharded for Hub limits.** Images are split into `shard_NNN/` subdirectories of ~3,000 files to stay under Hugging Face's practical per-directory file-count ceiling (train: 7 shards, valid: 2, test: 3). This has no effect on the data itself.
- No image pixel content was modified. No boxes were resized, moved, or reinterpreted.

---

# Dataset Structure

```text
<repo>/
├── README.md
├── animalsdet_banner.jpg
└── data/
    ├── data.yaml
    ├── images/
    │   ├── train/   shard_000 … shard_006/   (*.jpg + metadata.jsonl, ~3,000 per shard)
    │   ├── valid/   shard_000 … shard_001/
    │   └── test/    shard_000 … shard_002/
    └── labels/
        ├── train/   shard_000 … shard_006/   (*.txt, mirrors images)
        ├── valid/   shard_000 … shard_001/
        └── test/    shard_000 … shard_002/
```

where:

* `data/images/<split>/shard_NNN/` contains the RGB images, plus a `metadata.jsonl` (`file_name` and `objects.bbox` as absolute-pixel COCO `[x, y, w, h]` with `objects.categories`) that drives the Hugging Face dataset viewer.
* `data/labels/<split>/shard_NNN/` contains one YOLO-format `.txt` annotation file per image (`class x_center y_center width height`, normalized), mirroring the image shard layout.
* `data/data.yaml` is the Ultralytics dataset configuration file (class names, split paths, relative to `data/`).
* Splits: **train** 18,402 images / 24,091 boxes &middot; **valid** 3,247 images / 4,123 boxes &middot; **test** 6,003 images / 7,576 boxes (27,652 images / 35,790 boxes total).

### Classes (80)

Full taxonomy spans mammals (`Lion`, `Zebra`, `Elephant`, `Tiger`, `Giraffe`, ...), birds (`Eagle`, `Parrot`, `Owl`, `Sparrow`, ...), reptiles/amphibians (`Crocodile`, `Snake`, `Frog`, `Turtle`, ...), fish/marine (`Shark`, `Jellyfish`, `Seahorse`, `Sea lion`, ...), and invertebrates (`Butterfly`, `Spider`, `Crab`, `Snail`, ...). See [`docs/datasets/animalsdet`](https://github.com/dronefreak/DetectionBench/tree/main/docs/datasets/animalsdet) in the DetectionBench repository for the full per-class instance counts and box-geometry statistics.

Class distribution is heavily imbalanced: `Butterfly` is the most common class (2,930 boxes, 8.2%) while `Squid` is the rarest (29 boxes, 0.1%) -- roughly a 100x spread. Per-class metrics on the rarest classes are measured on very few examples.

---

# Dataset Sources

## Original Paper

**The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale**

Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari

*International Journal of Computer Vision*, 128:1956-1981, 2020. DOI: [10.1007/s11263-020-01316-z](https://doi.org/10.1007/s11263-020-01316-z)

## Official Resources

- **Official Website:** https://storage.googleapis.com/openimages/web/index.html
- **Official License Page:** https://storage.googleapis.com/openimages/web/factsfigures_v7.html
- **Download Toolkit Used:** https://github.com/EscVM/OIDv4_ToolKit

## Intermediate Export

- **Kaggle re-export:** https://www.kaggle.com/datasets/antoreepjana/animals-detection-images-dataset

---

# Attribution

**All credit for collecting and annotating this data belongs to Google (the Open Images project) and to the individual Flickr photographers who took the original photos.** This repository does not modify, reinterpret, or take credit for the underlying imagery or labels -- it only merges per-class-folder duplicates, converts to a YOLO-compatible layout, and adds a validation split.

If you use this dataset in your research, **please cite the original publication above.**

---

# License

**Not CC0, despite the intermediate Kaggle re-export's tag.** Open Images' own license page states the real terms:

> The annotations are licensed by Google LLC under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. The images are listed as having a [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) license, individually verifiable at the source Flickr pages linked from each image's Open Images metadata.

Source: https://storage.googleapis.com/openimages/web/factsfigures_v7.html (License section)

Accordingly:

- **Redistribution is explicitly permitted, with attribution**, for both the images (CC BY 2.0, per original photographer) and the box annotations (CC BY 4.0, Google) -- which is what this repository does.
- No share-alike obligation; no non-commercial restriction.
- This repository, and any further redistribution of it, should carry the same attribution.

---

# Citation

If you use this dataset, please cite:

```bibtex
@article{OpenImages,
  title={The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and Duerig, Tom and Ferrari, Vittorio},
  journal={IJCV},
  volume={128},
  number={7},
  pages={1956--1981},
  year={2020},
  publisher={Springer}
}
```

---

# Acknowledgements

We sincerely thank the Open Images team at Google, the original Flickr photographers whose images make up this dataset, and antoreepjana (Kaggle) and the OIDv4_ToolKit project for the per-class export this repository builds on.