Synthetic images of corals (Desmophyllum pertusum) with object detection models

Two object detection models using Darknet/YOLOv4 were trained on images of the coral Desmophyllum pertusum from the Kosterhavet National Park. In one of the models, the training image data was amplified using StyleGAN2 generative modeling. The dataset contains 2266 synthetic images with labels and 409 original images of corals used for training the ML model. Included is also the YOLOv4 models and the StyleGAN2 network. The still images were extracted from raw video data collected using a remotely operated underwater vehicle. 409 JPEG images from the raw video data are provided in 720x576 resolution. In certain images, coordinates visible in the OSD have been cropped. The synthetic images are PNG files in 512x512 resolution. The StyleGAN2 network is included as a serialized pickle file (*.pkl). The object detection models are provided in the .weights format used by the Darknet/YOLOv4 package. Two files are included (trained on original images only, trained on original + synthetic images). The machine learning software packages used is currently (2022) available on Github: StyleGAN2: https://github.com/NVlabs/stylegan2 YOLOv4: https://github.com/AlexeyAB/darknet本数据集基于取自瑞典科斯特哈维特国家公园（Kosterhavet National Park）的深杯珊瑚（Desmophyllum pertusum）影像，使用Darknet/YOLOv4框架训练了两款目标检测模型。其中一款模型的训练图像数据通过StyleGAN2生成式建模技术完成了数据扩增。 本数据集包含2266张带标注的合成图像，以及409张用于机器学习（Machine Learning，ML）模型训练的珊瑚原始影像，同时附带了两款YOLOv4模型与StyleGAN2网络。 所有静态图像均提取自遥控水下机器人（Remotely Operated Underwater Vehicle，ROV）采集的原始视频数据。本次提供的409张原始提取图像均为720×576分辨率的JPEG格式文件，部分图像中屏幕显示（On-Screen Display，OSD）的坐标信息已被裁剪。 合成图像均为512×512分辨率的PNG格式文件。 本次附带的StyleGAN2网络以序列化pickle文件（*.pkl）格式存储。 两款目标检测模型均采用Darknet/YOLOv4框架专用的.weights格式提供，共包含两个模型文件：分别为仅使用原始图像训练的模型，以及同时使用原始图像与合成图像训练的模型。 本数据集所用的机器学习软件包在2022年时可于GitHub平台获取： StyleGAN2: https://github.com/NVlabs/stylegan2 YOLOv4: https://github.com/AlexeyAB/darknet