Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
Authors/Creators
- 1. Swedish University of Agricultural Sciences
- 2. Stockholm University
Description
The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv4 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).
The data can be used and shared freely.
Notes
The dataset contains:
- Annotations (images and bounding box coordinates). The format is "YOLO Darknet TXT", described in detail here: https://roboflow.com/formats/yolo-darknet-txt
- The YOLOV4 model, 2 files: .cfg and .weights
- .csv outputs from the YOLO model (bounding boxes)
The videos themselves are several Tb (approx 1 Gb per H of film) and therefore not possible to share at this moment at a reasonable cost. Sample videos are provided as .mp4 files.
R-code for post-processing the model outputs including the generation of figures is available at https://github.com/BalticSeabird/ObjectDetectionInferences
Funding provided by: Svenska Forskningsrådet Formas
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001862
Award Number: 2021-02639
Funding provided by: Vetenskapsrådet
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004359
Award Number: 2021-03892
Funding provided by: Marcus och Amalia Wallenbergs minnesfond
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100011898
Award Number: 2018-0093
Files
Files
(256.1 MB)
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Additional details
Related works
- Is derived from
- https://github.com/BalticSeabird/ObjectDetectionInferences (URL)
- Is source of
- 10.5061/dryad.xsj3tx9hx (DOI)