The Animal10N Test Set consists of 10,000 images of animals from 10 different classes. The images are labeled with the animal's class.

This data contains 3 files consisting of the model weights of Inception, Xception and ResNet152V2. All the three models were trained on the Animal 10 classification dataset. Out of which, ResNet152V2 performed the best with 93% Training Accuracy and 92% Testing Accuracy with the lowest loss among all and converged to the solution the fastest. The other models are still not bad and they performed roughly close to it, around 91% accuracy in both training and testing part. That's why I have included both of them. If you can, check them out. The models predictions and the model itself is available in the Notebook associated with this data set.

Rapidata Animals-10

We took this existing Animals-10 dataset from kaggle and cleaned it using Rapidata's crowd, as detailed in this blog post. If you get value from this dataset and would like to see more in the future, please consider liking it.

  Dataset Details

10 classes: Butterfly, Cat, Chicken, Cow, Dog, Elephant, Horse, Sheep Spider, Squirrel 23554 Images In total, 124k labels were collected by human annotators, so each image is cross-validated on average by 5… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Animals-10.

dgrnd4/animals-10 dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Animals-10 Dataset is an image classification dataset composed of animal photos collected from Google Images. It includes images from 9 animal categories. 2) Data Utilization (1) Characteristics of the Animals-10 Dataset: • The dataset consists of real-world animal images taken under various backgrounds, angles, and lighting conditions, making it suitable for generalization experiments. • Some images intentionally include mislabeled samples to simulate realistic conditions and evaluate model robustness. (2) Applications of the Animals-10 Dataset: • Animal image classification model development: This dataset can be used to train deep learning-based classification models for building automated animal recognition systems useful for biologists and researchers.

Dataset

This dataset was created by Allen_002

Contents

Fake Animals

This dataset is a collection of 10 types of animal images created using Stable Diffusion 3.5 Large.

  Dataset Details







  Dataset Description

All images have a resolution of 1024x1024 and are divided into training and test sets with 1000 and 500 images, respectively. The classes are as follows:

'0': cat '1': dog '2': elephant '3': fish '4': giraffe '5': horse '6': lion '7': penguin '8': rabbit '9': tiger

The data was generated according to the… See the full description on the dataset page: https://huggingface.co/datasets/Prgckwb/fake-animals.

The file contains the data of tha animal activity registered during 6 cycles in a weaned piglet comercial farm (6-20kg body mass) using a passive infrared detector. Data were obtained in a 10min interval, following the method proposed in "https://doi.org/http://dx.doi.org/10.1016/j.biosystemseng.2017.06.014."Each cycle last for 40-42 days.The data were obtained with a PID (OPTEX RX-40QZ ) mounted at 2.8 m high over the entrance door in a weaned piglets room with 300 animals capacity. The room has a dimension of 12x6m.

The Animal Sound Archive at the Museum fuer Naturkunde Berlin (German: Tierstimmenarchiv) is one of the oldest and largest worldwide. Founded in 1951 by Professor Guenter Tembrock the collection consists now of around 130 000 records of animal voices.

Started out as a pumpkin detector to test training YOLOv5. Now suffering from extensive feature creep and probably ending up as a cat/dog/spider/pumpkin/randomobjects-detector. Or as a desaster.

The dataset does not fit https://docs.ultralytics.com/tutorials/training-tips-best-results/ well. There are no background images and the labeling is often only partial. Especially in the humans and pumpkin category where there are often lots of objects in one photo people apparently (and understandably) got bored and did not labe everything. And of course the images from the cat-category don't have the humans in it labeled since they come from a cat-identification model which ignored humans. It will need a lot of time to fixt that.

Dataset used: - Cat and Dog Data: Cat / Dog Tutorial NVIDIA Jetson https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md © 2016-2019 NVIDIA according to bottom of linked page - Spider Data: Kaggle Animal 10 image set https://www.kaggle.com/datasets/alessiocorrado99/animals10 Animal pictures of 10 different categories taken from google images Kaggle project licensed GPL 2 - Pumpkin Data: Kaggle "Vegetable Images" https://www.researchgate.net/publication/352846889_DCNN-Based_Vegetable_Image_Classification_Using_Transfer_Learning_A_Comparative_Study https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset Kaggle project licensed CC BY-SA 4.0 - Some pumpkin images manually copied from google image search - https://universe.roboflow.com/chess-project/chess-sample-rzbmc Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/steve-pamer-cvmbg/pumpkins-gfjw5 Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/nbduy/pumpkin-ryavl Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/homeworktest-wbx8v/cat_test-1x0bl/dataset/2 - https://universe.roboflow.com/220616nishikura/catdetector - https://universe.roboflow.com/atoany/cats-s4d4i/dataset/2 - https://universe.roboflow.com/personal-vruc2/agricultured-ioth22 - https://universe.roboflow.com/sreyoshiworkspace-radu9/pet_detection - https://universe.roboflow.com/artyom-hystt/my-dogs-lcpqe - license: Public Domain url: https://universe.roboflow.com/dolazy7-gmail-com-3vj05/sweetpumpkin/dataset/2 - https://universe.roboflow.com/tristram-dacayan/social-distancing-g4pbu - https://universe.roboflow.com/fyp-3edkl/social-distancing-2ygx5 License MIT - Spiders: https://universe.roboflow.com/lucas-lins-souza/animals-train-yruka

Currently I can't guarantee it's all correctly licenced. Checks are in progress. Inform me if you see one of your pictures and want it to be removed!

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12294787%2F2e9b3b5a8f236aab36655b4a0db4e311%2Foverview.jpg?generation=1718265309709943&alt=media" alt="drawing" style="width:700px;"/>

WildlifeReID-10k is a wildlife re-identification dataset with more than 140k images of 10k individual animals. It is a collection of 37 existing wildlife re-identification datasets with additional processing steps. WildlifeReID-10k contains animals as diverse as marine turtles, primates, birds, African herbivores, marine mammals and domestic animals. We provide a Jupyter notebook with introduction to the dataset, a way to evaluate developed algorithms and a baseline performance. WildlifeReID-10k has two primary uses:

Design an algorithm to classify individual animals in images. This is the much more complicated task (with 10k fine-grained classes) and the intended use of the dataset.

Design an algorithm to classify species of animals. This is a simpler task (with 20 coarse-grained classes) requiring fewer resources. It is intended for researchers or interested public who want to develop their first methods on an interesting dataset.

Animal Center Outcomes from Oct, 1st 2013 to May 5th 2025. Outcomes represent the status of animals as they leave the Animal Center. All animals receive a unique Animal ID during intake. Annually over 90% of animals entering the center, are adopted, transferred to rescue or returned to their owners. The Outcomes data set reflects that Austin, TX. is the largest "No Kill" city in the country.

Forecast: Share of Scientific Publications Among the World's 10% Top-Cited Publications in Animal Science and Zoology in the US 2024 - 2028 Discover more data with ReportLinker!

Animal Center Intakes from Oct, 1st 2013 to May, 5th 2025. Intakes represent the status of animals as they arrive at the Animal Center. All animals receive a unique Animal ID during intake. Annually over 90% of animals entering the center, are adopted, transferred to rescue or returned to their owners.

Introduction

This dataset supports Ye et al. 2024 Nature Communications. Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet that accompanies this download, including the meta data for how to use this data is 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).

https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1690988780004-AG00N6OU1R21MZ0AU9RE/modelcard-SAQ.png?format=1500w" target="_blank" rel="noopener">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 if you use this DATASET in your work.

References

1. Prianka Banik, Lin Li, and Xishuang Dong. A novel dataset for keypoint detection of quadruped animals from images. ArXiv, abs/2108.13958, 2021
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. iNaturalist. OGBIF Occurrence Download. https://doi.org/10.15468/dl.p7nbxt. iNaturalist, July 2020
9. 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.
10. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2016.
11. 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
12. 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

Versioning Note:

- V2 includes fixes to Stanford Dog data; it affected less than 1% of the data.

This data set is an image data set used to develop deep learning algorithms. The data was derived from ANIMAL-10N, where images were pulled from several online search engines, including Bing and Google, searched using predefined tags as search keywords, and then categorized by 15 recruited participants (10 undergraduate and 5 graduate students). Later, it was adapted by the Intelligent Education Center team of Shanghai Artificial Intelligence Laboratory, and now contains 8 categories (4 categories), the main format is ImageNet format. It contains four pairs of puzzling animals with a total of 39,607 images. The four pairs are: (cat, lynx), (Jaguar, cheetah), (wolf, coyote), (hamster, guinea pig).It can be applied to primary image classification algorithm training and testing.

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.

In response to the growing need to understand the response of plant and animal species to environmental variation and climate change and to develop a widespread baseline against which future phenological change may be measured, a consortium of scientists and agencies organized the USA-NPN, with a mandate to collect and share phenology data. More information on the USA-NPN can be found at http://www.usanpn.org/about.

As of January 1, 2013, the dataset contains phenology data on 591 species of plants and animals, with 7,512 locations registered across the United States. Protocols used are documented in Denny et. al., Submitted (contact nco@usanpn.org for more information). Data were collected using the phenophase status approach (Thomas et. al., 2010; Denny et. al. Submitted). Latitude and longitude given in WGS84 Datum. This is a suite of yearly data sets (Plants beginning in 2009, Animals beginning in 2010) each provided with and without full phenophase definitions.

Supplemental Information: Denny, E.G., K.L. Gerst, A.J. Miller-Rushing, G.L. Tierney, T.M. Crimmins, C.A.F. Enquist, P. Guertin, A.H. Rosemartin, M.D. Schwartz, K.A. Thomas and J.F. Weltzin. Submitted. Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications.

Thomas, K.A., E.G. Denny, A.J. Miller-Rushing, T.M. Crimmins, and J.F. Weltzin. 2010. The National Phenology Monitoring System v0.1. USA-NPN Technical Series 2010-001. www.usanpn.org.

This dataset contains different animal categories such as: buffalo, capybara, cat, cow, deer, dog, elephant, flamingo, giraffe, jaguar, kangaroo, lion, parrot, penguin, rhino, sheep, tiger, turtle and zebra.

Most of the images can be found in existing datasets: https://github.com/freds0/capybara_dataset https://universe.roboflow.com/miguel-narbot-usp-br/capybara-and-animals/dataset/1 https://www.kaggle.com/datasets/hugozanini1/kangaroodataset?resource=download https://github.com/experiencor/kangaroo https://universe.roboflow.com/z-jeans-pig/kangaroo-epscj/dataset/1 https://cvwc2019.github.io/challenge.html# https://www.kaggle.com/datasets/biancaferreira/african-wildlife https://universe.roboflow.com/new-workspace-5kofa/elephant-dataset/dataset/6 https://universe.roboflow.com/nathanael-hutama-harsono/large-cat/dataset/1/images/?split=train https://universe.roboflow.com/giraffs-and-cows/giraffes-and-cows/dataset/1 https://universe.roboflow.com/turtledetector/turtledetector/dataset/2 https://www.kaggle.com/datasets/smaranjitghose/sea-turtle-face-detection https://universe.roboflow.com/fadilyounes-me-gmail-com/zebra---savanna/dataset/1 https://universe.roboflow.com/test-qeryf/yolov5-9snhq https://universe.roboflow.com/or-the-king/two-zebras https://universe.roboflow.com/wild-animals-datasets/zebra-images/dataset/2 https://universe.roboflow.com/zebras/zebras/dataset/2 https://universe.roboflow.com/v2-rabotaem-xkxra/zebras_v2/dataset/5 https://universe.roboflow.com/vijay-vikas-mangena/animal_od_test1/dataset/1 https://universe.roboflow.com/bdoma13-gmail-com/rhino_horn/dataset/7 https://universe.roboflow.com/rudtkd134-naver-com/finalproject2/dataset/2 https://universe.roboflow.com/the-super-nekita/cats-brofl/dataset/2 https://universe.roboflow.com/lihi-gur-arie/pinguin-object-detection/dataset/2 https://universe.roboflow.com/utas-377cc/penguindataset-4dujc/dataset/10 https://universe.roboflow.com/new-workspace-tdyir/penguin-clfnj/dataset/1 https://universe.roboflow.com/utas-wd4sd/kit315_assignment/dataset/7 https://universe.roboflow.com/jeonjuuniv/deer-hqp4i/dataset/1 https://universe.roboflow.com/new-workspace-hqowp/sheeps/dataset/1 https://universe.roboflow.com/ali-eren-altindag/sheepstest2/dataset/1 https://universe.roboflow.com/yaser/sheep-0gudu/dataset/3 https://universe.roboflow.com/ali-eren-altindag/mixed_sheep/dataset/1 https://universe.roboflow.com/pkm-kc-2022/sapi-birahi/dataset/2 https://universe.roboflow.com/ghostikgh/team1_cows/dataset/5 https://universe.roboflow.com/ml-dlq4x/liontrain/dataset/2 https://universe.roboflow.com/animals/lionnew/dataset/2 https://universe.roboflow.com/parrottrening/parrot_trening/dataset/1 https://universe.roboflow.com/uet-hi8bg/parrots-r4tfl/dataset/1 https://universe.roboflow.com/superweight/parrot_poop/dataset/5 https://www.kaggle.com/datasets/tarunbisht11/intruder-detection

From those datasets the images has been filtered (deleted objects of size smaller 32, images with dimension smaller than 320px has been deleted, images and labeled objects has been renamed). The rest of images has been labeled by me.

*includes singleton gene families.