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Check out the documentation for more information.
Quail Egg Detection Dataset
Overview
This dataset was meticulously curated as part of a school project aimed at developing a machine learning model capable of accurately detecting quail eggs. The motivation stemmed from the practical need to automate the process of egg detection within a henhouse environment. Leveraging both images captured in the henhouse and those obtained from the classroom setting, this dataset represents a diverse range of real-world scenarios.
Data Collection
The dataset comprises images sourced from multiple environments, including both controlled settings such as classrooms and the dynamic environment of a working henhouse. These images were carefully selected to encompass various lighting conditions, backgrounds, and perspectives to ensure robustness in model training.
Annotation
All images in this dataset have been meticulously annotated utilizing the free version of the online annotation tool 'Roboflow'. Each quail egg instance within the images is precisely outlined using bounding boxes. Notably, a subset of images features irregular shapes, necessitating polygonal annotations. While traditional training processes often disregard such images, this dataset includes them to enhance model adaptability to real-world complexities.
Dataset Format
The dataset includes 5776 images. Eggs-quails are annotated in YOLOv8 format.
The following pre-processing was applied to each image:
The following augmentation was applied to create 3 versions of each source image:
- Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise
- Random shear of between -15° to +15° horizontally and -10° to +10° vertically
- Salt and pepper noise was applied to 1.95 percent of pixels
The following transformations were applied to the bounding boxes of each image:
- Random brigthness adjustment of between -27 and +27 percent
Usage
Researchers and developers interested in quail egg detection, object detection in dynamic environments, or training YOLOv8 models can leverage this dataset for experimentation and model development. The diversity of environments and annotation complexities makes this dataset invaluable for advancing object detection capabilities in challenging real-world scenarios.
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