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Hoofed Animals Dataset

20061
Taggeneral
Taskinstance segmentation
Release YearMade in 2007
Licenseunknown

Introduction #

Narendra Ahuja, Sinisa Todorovic

The Hoofed Animals dataset is tailored for instance segmentation, semantic segmentation, and object detection tasks, comprising 200 images that collectively feature 714 labeled objects distributed among 6 distinct classes: cow, horse, sheep, along with additional categories like deer, goat, and camel. Notably, this dataset presents a challenge due to the subtle cross-category differences among similar animal categories, making it complex for algorithms to distinguish between them accurately. The animals within the dataset are articulated and non-rigid, appearing at various scales and often partially occluded within cluttered environments, posing additional challenges for effective detection and segmentation.

The hoofed animals dataset is designed to complement currently popular benchmarks, such as Caltech-256 and PASCAL. The major deficiencies of these datasets are that their images typically contain a single, prominently featured object from an object category, and that the categories used significantly differ in appearance and topology. In contrast, the hoofed animals dataset contains very similar categories, and therefore poses a challenge for an algorithm to resolve subtle cross-category differences. Since the animals are similar, they share a number of similar parts (e.g., horses and deer have similar limbs). Also, the animals have category-specific, discriminative subparts which allow for categorization (e.g., only deer have antlers). This makes the dataset suitable for evaluating which inter-category relationships an algorithm is capable of capturing (e.g., similarity in terms of shared parts, taxonomy in terms of shared and unshared parts, etc.). Another increase in complexity over popular benchmark datasets is that each image may contain multiple instances of multiple categories. Other challenges involve the following: the animals are articulated, non-rigid objects; they appear at different scales across the dataset; and may be partially occluded, amidst clutter.

These figures show detection, recognition, and segmentation on the test images shown in (third image; rows 1,3) using the DAG (Directed
Acyclic Graph) learned on the training images shown in the first image. The DAG successfully resolves the subtle differences among the animals, since it learns the subcategories of each of these six that are not shared, namely: camel’s hump and head, cow’s udder and head, deer’s antlers, goat’s beard and horns, horse’s reins, and mane; shown in second image are the parts of the test images in third image that got matched with the non-shared DAG nodes.

hoofed_animals_preview_1

Sample training images from Hoofed Animals: each image may contain multiple instances of very similar animals, posing a challenge for cross-category resolvability

hoofed_animals_preview_2

Learned discriminative, non-shared subcategories of the target categories.

hoofed_animals_preview_3

Masked out are the image parts not occupied by detected instances of target categories.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Hoofed Animals is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

The dataset consists of 200 images with 714 labeled objects belonging to 6 different classes including cow, horse, sheep, and other: deer, goat, and camel.

Images in the Hoofed Animals dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2007 by the University of Illinois at Urbana-Champaign, USA.

Here are the visualized examples for the classes:

Explore #

Hoofed Animals dataset has 200 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
OpenSample annotation mask from Hoofed AnimalsSample image from Hoofed Animals
👀
Have a look at 200 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-6 of 6
Class
Images
Objects
Count on image
average
Area on image
average
cow
mask
63
169
2.68
13.35%
horse
mask
48
89
1.85
10.82%
sheep
mask
41
135
3.29
11.84%
deer
mask
41
79
1.93
17.24%
goat
mask
39
135
3.46
14.17%
camel
mask
28
107
3.82
12.94%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-6 of 6
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
cow
mask
169
4.98%
31.14%
0.35%
23px
6.87%
297px
81.55%
97px
29.48%
32px
4.28%
448px
89.6%
sheep
mask
135
3.6%
30.98%
0.19%
19px
6.62%
344px
67.08%
84px
24.29%
24px
4.25%
493px
82%
goat
mask
135
4.09%
26.27%
0.28%
21px
7.4%
318px
85%
122px
32.76%
38px
6.95%
327px
79.21%
camel
mask
107
3.39%
21.95%
0.31%
34px
9.84%
385px
82.33%
118px
31.91%
23px
6.05%
424px
79.25%
horse
mask
89
5.84%
34.64%
0.48%
16px
9.52%
199px
80.41%
108px
37.26%
31px
7.95%
365px
84.7%
deer
mask
79
8.95%
26.93%
1.21%
37px
19.59%
534px
95.11%
155px
54.56%
25px
10%
608px
95.08%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 714 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 714
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
goat
mask
54.png
274 x 375
79px
28.83%
107px
28.53%
4.25%
2
goat
mask
54.png
274 x 375
84px
30.66%
78px
20.8%
3.6%
3
goat
mask
54.png
274 x 375
113px
41.24%
144px
38.4%
7.85%
4
cow
mask
196.png
336 x 448
75px
22.32%
116px
25.89%
2.23%
5
cow
mask
196.png
336 x 448
81px
24.11%
123px
27.46%
3.56%
6
cow
mask
196.png
336 x 448
69px
20.54%
112px
25%
3.18%
7
cow
mask
196.png
336 x 448
92px
27.38%
100px
22.32%
3.4%
8
cow
mask
194.png
694 x 1149
159px
22.91%
215px
18.71%
2.64%
9
cow
mask
194.png
694 x 1149
148px
21.33%
227px
19.76%
2.17%
10
cow
mask
194.png
694 x 1149
188px
27.09%
264px
22.98%
3.7%

License #

License is unknown for the Hoofed Animals dataset.

Source

Citation #

If you make use of the Hoofed Animals data, please cite the following reference:

@dataset{Hoofed Animals,
  author={Narendra Ahuja and Sinisa Todorovic},
  title={Hoofed Animals},
  year={2007},
  url={https://web.engr.oregonstate.edu/~sinisa/HoofedAnimalsDataset.html}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-hoofed-animals-dataset,
  title = { Visualization Tools for Hoofed Animals Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/hoofed-animals } },
  url = { https://datasetninja.com/hoofed-animals },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

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