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Goat Image Dataset

176741
Taglivestock, biology
Taskobject detection
Release YearMade in 2020
LicenseCC BY 4.0
Download506 MB

Introduction #

Released 2020-09-17 ·Masum Billah, Yu Jiantao, Yu Jiang

The authors of the Goat Images Dataset emphasize the significance of automatic individual animal identification in the context of livestock farming, with its potential to enhance breeding histories and contribute substantially to breeding and genetic management programs. In the current livestock identification practices, such as the utilization of various tags, tattoos, paint brands, and microchips, manual identification processes prevail, characterized by their time-consuming, expensive, and unreliable nature. In their research, authors present a deep learning-based solution, offering a fully automated pipeline for the detection and recognition of goats’ faces, a task more complex than human face recognition due to the high similarity among individual goats.

In the broader context of livestock farming, the automatic identification of individual animals has become a pivotal element, enabling comprehensive data collection and report generation to support farm management. While historical methods like tag identification have been employed, they pose challenges when applied to animals kept in groups, such as goats, as individual observation and data collection become arduous. To overcome this challenge, researchers are turning to deep learning techniques, which have found application in various aspects of animal farming, including biometric identification, activity monitoring, body condition scoring, and behavioral analysis.

The use of deep learning algorithms is gaining traction across the animal industry, addressing identification challenges by focusing on specific body parts, such as sheep retinas, cattle body patterns, and pig faces. However, challenges persist in obtaining clear and comprehensive datasets for these methods in real-world, uncontrolled environments. As of January 2022, the performance of CNNs for individual goat identification using facial images remains unexplored. The paper aims to extract robust features that enhance the distinguishability between individual goats in facial images. The proposed methodology comprises two primary steps: face detection and identity recognition. Given an input image, the first step involves the detection of faces and facial landmarks using a Yolo-based method, followed by cropping these regions into fixed segments. Facial landmarks serve to geometrically normalize the face. Ultimately, a custom CNN model extracts features for the detected regions and predicts classification probabilities. Notably, the approach covers not only face recognition but also automatic face detection and alignment based on the eye centroid, akin to human face recognition.

Two different types of image datasets are uploaded. The first dataset (dataset_1) is useful for detection purpose. This consists of a total 1680 goat image where the face, eye, mouth and ear bounding boxes are given in YOLO format. On the other hand, the second dataset (dataset_2) is for face recognition and facial expression analysis. In total 1311 images are captured from 10 individuals of a Chinese farm (34.2721° N, 108.0847° E).

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Goat Image Dataset is a dataset for object detection and identification tasks. It is used in the livestock industry, and in the biological research.

The dataset consists of 1767 images with 13761 labeled objects belonging to 4 different classes including face, ear, eye, and other: mouth.

Images in the Goat Image dataset have bounding box annotations. There are 87 (5% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: dataset_1 (1680 images) and dataset_2 (87 images). Additionally, every image in dataset_2 contains information about goat_id. The dataset was released in 2020 by the Northwest A&F University, China.

Dataset Poster

Explore #

Goat Image dataset has 1767 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 Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
OpenSample annotation mask from Goat ImageSample image from Goat Image
👀
Have a look at 1767 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
Images
Objects
Count on image
average
Area on image
average
face
rectangle
1680
3078
1.83
6.56%
ear
rectangle
1655
4771
2.88
3.09%
eye
rectangle
1632
3326
2.04
0.57%
mouth
rectangle
1628
2586
1.59
1.13%

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 1-4 of 4
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
ear
rectangle
4771
1.08%
43.97%
0.01%
6px
0.82%
801px
83.07%
94px
11.11%
6px
0.68%
542px
52.93%
eye
rectangle
3326
0.28%
19.67%
0.01%
6px
0.94%
482px
62.76%
41px
5.02%
7px
0.73%
321px
31.35%
face
rectangle
3078
3.61%
85.28%
0.01%
8px
1.11%
860px
100%
152px
18.62%
6px
0.62%
881px
92.99%
mouth
rectangle
2586
0.71%
22.59%
0.01%
5px
0.78%
445px
65.15%
58px
7.15%
6px
0.77%
380px
39.97%

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 13761 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 13761
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
face
rectangle
384.jpg
720 x 960
206px
28.61%
184px
19.17%
5.48%
2
face
rectangle
384.jpg
720 x 960
95px
13.19%
130px
13.54%
1.79%
3
face
rectangle
384.jpg
720 x 960
78px
10.83%
124px
12.92%
1.4%
4
eye
rectangle
384.jpg
720 x 960
62px
8.61%
58px
6.04%
0.52%
5
eye
rectangle
384.jpg
720 x 960
35px
4.86%
29px
3.02%
0.15%
6
eye
rectangle
384.jpg
720 x 960
31px
4.31%
39px
4.06%
0.17%
7
mouth
rectangle
384.jpg
720 x 960
65px
9.03%
71px
7.4%
0.67%
8
ear
rectangle
384.jpg
720 x 960
176px
24.44%
85px
8.85%
2.16%
9
mouth
rectangle
384.jpg
720 x 960
32px
4.44%
36px
3.75%
0.17%
10
mouth
rectangle
384.jpg
720 x 960
46px
6.39%
44px
4.58%
0.29%

License #

Goat Image Dataset is under CC BY 4.0 license.

Source

Citation #

If you make use of the Goat Image data, please cite the following reference:

Billah, Masum; Jiantao, Yu; Jiang, Yu (2020), 
“Goat Image Dataset”, 
Mendeley Data, V2, 
doi: 10.17632/4skwhnrscr.2

Source

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

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

Download #

Dataset Goat Image can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Goat Image', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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