task_categories:
- image-classification
language:
- en
tags:
- FGVC
pretty_name: BOATS
size_categories:
- 10K<n<100K
Dataset Card for BOATS Dataset
Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
Gaining Access
If you want to gain access to the dataset send me a message on LinkedIn www.linkedin.com/in/cringgaard
Dataset Summary
A dataset consisting of over a 100.000 images of 7108 classes of sailboats from small dinghies to large ships. The hull type, rigging, construction material and ballast type have also been noted.
This dataset was used for my bachelor's thesis in data science and machine learning at the university of Copenhagen. The images were scraped from the web as specified in the thesis.
Abstract
Modern computer vision models are trained on large datasets but typically require fine-tuning to perform well on specific tasks. These tasks are often valuable on their own but can also be used for evaluating the pre-trained representations. In this thesis one such dataset is presented, a fine-grained sailboat dataset with annotated attributes. The dataset is suitable as starting point for the automation of classifying sailboats, which can be of value for e.g., monitoring marinas and sensitive waters. The data also presents new challenges to computer vision models in how it requires the classification of attributes that are not directly visible due to being submerged in water. While there is a limited amount of images available for each boat type, collectively the different boat attributes are present in multiple images. To further expand the dataset, a weakly labeled extension is scraped via an image search on a popular search engine. We train a variety of classifiers over the dataset and explore if the models benefit from learning to classify specific attributes in combination with others. Our findings show that using such multitask learning and a weakly labelled large dataset, it is possible to create a model capable of inferring the exact boat type at least as well as a human with expertise in the area.