# AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

arXiv: https://arxiv.org/abs/1909.04951 · HTML: https://mlsanity.com/paper/1909.04951/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 28 of 28)

- [VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks](https://mlsanity.com/paper/2406.08394/) (2024)
- [Pose for Everything: Towards Category-Agnostic Pose Estimation](https://mlsanity.com/paper/2207.10387/) (2022)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild](https://mlsanity.com/paper/2103.13282/) (2021)
- [X-Pose: Detecting Any Keypoints](https://mlsanity.com/paper/2310.08530/) (2023)
- [NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research](https://mlsanity.com/paper/2211.11747/) (2022)
- [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://mlsanity.com/paper/2407.13555/) (2024)
- [On Label Granularity and Object Localization](https://mlsanity.com/paper/2207.10225/) (2022)
- [Forensic Analysis of Synthetically Generated Western Blot Images](https://mlsanity.com/paper/2112.08739/) (2021)
- [Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching](https://mlsanity.com/paper/2310.05056/) (2023)
- [CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation](https://mlsanity.com/paper/2406.00384/) (2024)
- [CatFLW: Cat Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2305.04232/) (2023)
- [KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension](https://mlsanity.com/paper/2411.01846/) (2024)
- [Seeing Faces in Things: A Model and Dataset for Pareidolia](https://mlsanity.com/paper/2409.16143/) (2024)
- [Automated Detection of Cat Facial Landmarks](https://mlsanity.com/paper/2310.09793/) (2023)
- [DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation](https://mlsanity.com/paper/2107.07330/) (2021)
- [Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation](https://mlsanity.com/paper/2108.13258/) (2021)
- [Multi-Domain Multi-Definition Landmark Localization for Small Datasets](https://mlsanity.com/paper/2203.10358/) (2022)
- [CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark](https://mlsanity.com/paper/2406.03431/) (2024)
- [Object Recognition Datasets and Challenges: A Review](https://mlsanity.com/paper/2507.22361/) (2025)
- [Unsupervised Landmark Discovery Using Consistency Guided Bottleneck](https://mlsanity.com/paper/2309.10518/) (2023)
- [DogFLW: Dog Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2405.11501/) (2024)
- [Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision](https://mlsanity.com/paper/2107.01824/) (2021)
- [Face processing emerges from object-trained convolutional neural networks](https://mlsanity.com/paper/2405.18800/) (2024)
- [Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI](https://mlsanity.com/paper/2505.21589/) (2025)
- [KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model](https://mlsanity.com/paper/2507.11102/) (2025)
- [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026)
- [GKDT: General Keypoint Detection Transformer](https://mlsanity.com/paper/2607.00752/) (2026)

## Abstract (arXiv)

Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed through their faces. Though remarkable progress has been made towards the automatic understanding of human faces, this has regrettably not been the case with animal faces. There exists significant room and appropriate need to develop automatic systems capable of interpreting animal faces. Among many transformative impacts, such a technology will foster better and cheaper animal healthcare, and further advance animal psychology understanding. We believe the underlying research progress is mainly obstructed by the lack of an adequately annotated dataset of animal faces, covering a wide spectrum of animal species. To this end, we introduce a large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours. We benchmark it for face alignment using the existing art under novel problem settings. Results showcase its challenging nature, unique attributes and present definite prospects for novel, adaptive, and generalized face-oriented CV algorithms. We further benchmark the dataset for face detection and fine-grained recognition tasks, to demonstrate multi-task applications and room for improvement. Experiments indicate that this dataset will push the algorithmic advancements across many related CV tasks and encourage the development of novel systems for animal facial behaviour monitoring. We will make the dataset publicly available.