vapaau/jambo

JAMBO数据集包含3290张由ROV在丹麦日德兰半岛西北部Jammer湾温水区域拍摄的海底水下图像。所有图像经过六位注释者的标注，分为沙、石、不良三个类别。沙底栖息地主要特点是沙或泥沙，含粘土量小于5%，石头、植被和牡蛎床的覆盖率小于30%。石质礁栖息地特点是海床覆盖石头或巨石超过30%。不良类别用于标注因图像质量差、浑浊度等原因无法确信属于上述栖息地类型的图像。每位注释者为每张图像都提供了标注，共有六个独立的标注，可用于分析注释者间的分歧如何影响机器学习模型的性能。提供了交叉验证划分和基于日期的划分的数据集划分文件。更多信息及基线模型请参考ECCV 2024生态学计算机视觉研讨会上的论文。

The JAMBO dataset contains 3290 underwater images of the seabed captured by an ROV in temperate waters in the Jammer Bay area off the North West coast of Jutland, Denmark. All images are annotated by six annotators into three classes: sand, stone, or bad. Sand habitats are primarily sand or muddy sand with less than 5% clay and less than 30% cover of stones/boulders, vegetation, and mussel bed. Stone reef habitats are characterized by more than 30% seabed cover of stones or boulders. The bad class is used for images that cannot be confidently annotated as one of the aforementioned habitat types due to poor image quality, turbidity, or similar. Each of the six annotators has provided individual annotations for all images, allowing for analysis of how inter-annotator disagreement affects the performance of machine learning models. Cross-validation splits and date-based splits are provided in the dataset. For more information about the dataset and baseline models, please refer to the paper presented at the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop.