--- 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: - 10KAnimals Detection Images Dataset (Object Detection)

Animals Detection Dataset Banner

![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 ` ` 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 / ├── 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//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//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 · **valid** 3,247 images / 4,123 boxes · **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.