AP-10K is the first large-scale benchmark for general animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 60 species following the taxonomic rank and high-quality keypoint annotations labeled and checked manually. Source: AP-10K: A Benchmark for Animal Pose Estimation in the Wild Image source: https://arxiv.org/pdf/2108.12617v1.pdf

Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footage used in the dataset records different times of the day in an extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, the dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.

Animal-Pose Dataset is an animal pose dataset to facilitate training and evaluation. This dataset provides animal pose annotations on five categories are provided: dog, cat, cow, horse, sheep, with in total 6,000+ instances in 4,000+ images. Besides, the dataset also contains bounding box annotations for other 7 animal categories. Source: Cross-Domain Adaptation for Animal Pose Estimation Image Source: https://sites.google.com/view/animal-pose/

An 'in the wild' dataset of 20,580 dog images for which 2D joint and silhouette annotations were collected. Source: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop

Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. However, research in this area is held back by the lack of a comprehensive and diverse dataset with high-quality 3D pose and shape annotations. In this paper, we propose Animal3D, the first comprehensive dataset for mammal animal 3D pose and shape estimation. Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model. All annotations were labeled and checked manually in a multi-stage process to ensure highest quality results. Based on the Animal3D dataset, we benchmark representative shape and pose estimation models at: (1) supervised learning from only the Animal3D data, (2) synthetic to real transfer from synthetically generated images, and (3) fine-tuning human pose and shape estimation models. Our experimental results demonstrate that predicting the 3D shape and pose of animals across species remains a very challenging task, despite significant advances in human pose estimation. Our results further demonstrate that synthetic pre-training is a viable strategy to boost the model performance. Overall, Animal3D opens new directions for facilitating future research in animal 3D pose and shape estimation, and is publicly available.

The ATRW Dataset contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. Source: ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

Horse-10 is an animal pose estimation dataset. It comprises 30 diverse Thoroughbred horses, for which 22 body parts were labeled by an expert in 8,114 frames (animal pose estimation). Horses have various coat colors and the “in-the-wild” aspect of the collected data at various Thoroughbred yearling sales and farms added additional complexity. The authors introduce Horse-C to contrast the domain shift inherent in the Horse-10 dataset with domain shift induced by common image corruptions.

AwA Pose is a large scale animal keypoint dataset with ground truth annotations for keypoint detection of quadruped animals from images.

The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals. Typically, datasets for animal-specific vision tasks are created using open-source video material. This might be effective for an initial start, but these methods are not deployment ready for the behavior community. Therefore, we designed a semi-automated method for biologists to create well-curated datasets at a large scale for the ML and Vision community. 3D-POP is the first dataset with 3D ground truth for multi-animal, multi-view tracking problems. Highlight: The dataset is captured with the intention of using it for various vision problems and with different levels of complexity (no of cameras, no of individuals) Video explanation: Link to YouTube video Video teaser: Link to YouTube video ## Dataset Features: ### Marker-based videos: - 6 hours+ of annotations of 18 individuals (groups of 1, 2, 5, 10). - Bounding box - Trajectories (2D and 3D) - Posture (2D and 3D) with 9 key points - Identities - Total of 57 sequences (4K) with 4 views. - Dataset customization* (Users can modify the dataset and add key points to the dataset) ### Markerless: - 1Hr+ videos of 18 individuals in groups of 1, 2, 5, 11. The birds have no markers. This data is provided as test cases and unsupervised approaches. ## Problems: ### 2D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### 3D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### Fine-grained recognition: - Identity tracking with ground truth. ### Unsupervised learning: - 2D or 3D posture problems ## Idea: The dataset is created with a motion capture system, using the 6-DOF tracking ability. Assumptions are that head and body act as rigid bodies when birds walk and forage (proved with experiment). Therefore, we get the 3D position of key points by tracking head/body orientation.

Introduction This dataset supports Ye et al. 2024 Nature Communications. Ye, S., Filippova, A., Lauer, J. et al. SuperAnimal pretrained pose estimation models for behavioral analysis. Nat Commun 15, 5165 (2024). https://doi.org/10.1038/s41467-024-48792-2 Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet accompanying this download, including the metadata for how to use this data if you want to compare model results on benchmark tasks. Below is just a summary. Also see the dataset licensing below. # Training Data It consists of being trained together on the following datasets: AwA-Pose Quadruped dataset, see full details at (1). AnimalPose See full details at (2). AcinoSet See full details at (3). Horse-30 Horse-30 dataset, benchmark task is called Horse-10; See full details at (4). StanfordDogs See full details at (5, 6). AP-10K See full details at (7). iRodent We utilized the iNaturalist API functions for scraping observations with the taxon ID of Suborder Myomorpha (8). The functions allowed us to filter the large amount of observations down to the ones with photos under the CC BY-NC creative license. The most common types of rodents from the collected observations are Muskrat (Ondatra zibethicus), Brown Rat (Rattus norvegicus), House Mouse (Mus musculus), Black Rat (Rattus rattus), Hispid Cotton Rat (Sigmodon hispidus), Meadow Vole (Microtus pennsylvanicus), Bank Vole (Clethrionomys glareolus), Deer Mouse (Peromyscus maniculatus), White-footed Mouse (Peromyscus leucopus), Striped Field Mouse (Apodemus agrarius). We then generated segmentation masks over target animals in the data by processing the media through an algorithm we designed that uses a Mask Region Based Convolutional Neural Networks(Mask R-CNN) (9) model with a ResNet-50-FPN backbone (10), pretrained on the COCO datasets (11). The processed 443 images were then manually labeled with both pose annotations and segmentation masks. iRodent data is banked at https://zenodo.org/record/8250392. APT-36K See full details at (12). Here is an image with a keypoint guide. #Ethical Considerations • No experimental data was collected for this model; all datasets used are cited above. #Caveats and Recommendations • Please note that each dataest was labeled by separate labs & separate individuals, therefore while we map names to a unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2024 for our Supplementary Note on annotator bias). You will also note the dataset is highly diverse across species, but collectively has more representation of domesticated animals like dogs, cats, horses, and cattle. We recommend if performance of a model trained on this data is not as good as you need it to be, first try video adaptation (see Ye et al. 2024), or fine-tune the weights with your own labeling. #License Modified MIT. Copyright 2023-present by Mackenzie Mathis, Shaokai Ye, and contributors. Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive, and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”) to use the "DATASET" subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software: This data or resulting software may not be used to harm any animal deliberately. LICENSEE acknowledges that the DATASET is a research tool. THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET. If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis (mackenzie@post.harvard.edu) for a commercial use license. Please cite Ye et al 2024 if you use this DATASET in your work. #References Prianka Banik, Lin Li, and Xishuang Dong. A novel dataset for keypoint detection of quadruped animals from images. ArXiv, abs/2108.13958, 2021 Jinkun Cao, Hongyang Tang, Haoshu Fang, Xiaoyong Shen, Cewu Lu, and Yu-Wing Tai. Cross-domain adaptation for animal pose estimation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9497–9506, 2019. Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fred Nicolls, Alexander Mathis, Mackenzie W. Mathis, and Amir Patel. Acinoset: A 3d pose estimation dataset and baseline models for cheetahs in the wild. 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13901–13908, 2021. Alexander Mathis, Thomas Biasi, Steffen Schneider, Mert Yuksekgonul, Byron Rogers, Matthias Bethge, and Mackenzie W Mathis. Pretraining boosts out-of-domain robustness for pose estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1859–1868, 2021. Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011. Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon, and Roberto Cipolla. Creatures great and smal: Recovering the shape and motion of animals from video. In Asian Conference on Computer Vision, pages 3–19. Springer, 2018. Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, and Dacheng Tao. Ap-10k: A benchmark for animal pose estimation in the wild. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. iNaturalist. OGBIF Occurrence Download. https://doi.org/10.15468/dl.p7nbxt. iNaturalist, July 2020 Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2016. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’ar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014 Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, and Dacheng Tao. Apt-36k: A large-scale benchmark for animal pose estimation and tracking. Advances in Neural Information Processing Systems, 35:17301–17313, 2022

Three wild-type (C57BL/6J) male mice ran on a paper spool following odor trails (Mathis et al 2018). These experiments were carried out in the laboratory of Venkatesh N. Murthy at Harvard University. Data were recorded at 30 Hz with 640 x 480 pixels resolution acquired with a Point Grey Firefly FMVU-03MTM-CS. One human annotator was instructed to localize the 12 keypoints (snout, left ear, right ear, shoulder, four spine points, tail base and three tail points). All surgical and experimental procedures for mice were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Institutional Animal Care and Use Committee. 161 frames were labeled, making this a real-world sized laboratory dataset.

Schools of inland silversides (Menidia beryllina, n=14 individuals per school) were recorded in the Lauder Lab at Harvard University while swimming at 15 speeds (0.5 to 8 BL/s, body length, at 0.5 BL/s intervals) in a flow tank with a total working section of 28 x 28 x 40 cm as described in previous work, at a constant temperature (18±1°C) and salinity (33 ppt), at a Reynolds number of approximately 10,000 (based on BL). Dorsal views of steady swimming across these speeds were recorded by high-speed video cameras (FASTCAM Mini AX50, Photron USA, San Diego, CA, USA) at 60-125 frames per second (feeding videos at 60 fps, swimming alone 125 fps). The dorsal view was recorded above the swim tunnel and a floating Plexiglas panel at the water surface prevented surface ripples from interfering with dorsal view videos. Five keypoints were labeled (tip, gill, peduncle, dorsal fin tip, caudal tip). 100 frames were labeled, making this a real-world sized laboratory dataset.

Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models. To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task. We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.

All animal procedures are overseen by veterinary staff of the MIT and Broad Institute Department of Comparative Medicine, in compliance with the NIH guide for the care and use of laboratory animals and approved by the MIT and Broad Institute animal care and use committees. Video of common marmosets (Callithrix jacchus) was collected in the laboratory of Guoping Feng at MIT. Marmosets were recorded using Kinect V2 cameras (Microsoft) with a resolution of 1080p and frame rate of 30 Hz. After acquisition, images to be used for training the network were manually cropped to 1000 x 1000 pixels or smaller. The dataset is 7,600 labeled frames from 40 different marmosets collected from 3 different colonies (in different facilities). Each cage contains a pair of marmosets, where one marmoset had light blue dye applied to its tufts. One human annotator labeled the 15 marker points on each animal present in the frame (frames contained either 1 or 2 animals). https://benchmark.deeplabcut.org/datasets.html

Dataset page: https://github.com/mosamdabhi/MBW-Data MBW - Zoo is a challenging dataset consisting image frames of tail-end distribution categories (such as Fish, Colobus Monkeys, Chimpanzees, etc.) with their corresponding 2D, 3D, and Bounding-Box labels generated from minimal human intervention. Some of the prominent use cases of this dataset include not only sparse 2D and 3D landmark prediction. The data was collected by two smartphone cameras without any constraints: meaning no guidance or instructions were given as to how the data should be collected. The intention was to mimic the data captured casually by anyone holding a smartphone grade camera. Due to this reason, the cameras were continuously moving in space changing their extrinsics with respect to each other, capturing an in-the-wild dynamic scene. This dataset could be used to benchmark robust algorithms in various computer vision tasks.

Desert Locus is a animal pose estimation dataset for desert locuses.

MacaquePose is an animal pose estimation dataset containing pictures of macaque monkeys and manually labeled annotations on them.

Vinegar Fly is a pose estimation dataset for fruit flies.

Grévy’s Zebra is an animal pose estimation dataset for zebras.