| --- |
| 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> |
|
|
|  |
|  |
|  |
|  |
|  |
|  |
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|
| > **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 · **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. |
|
|
