Image Categorization

A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available. Source: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

VegFru is a domain-specific dataset for fine-grained visual categorization. VegFru categorizes vegetables and fruits according to their eating characteristics, and each image contains at least one edible part of vegetables or fruits with the same cooking usage. Particularly, all the images are labelled hierarchically. The current version covers vegetables and fruits of 25 upper-level categories and 292 subordinate classes. And it contains more than 160,000 images in total and at least 200 images for each subordinate class. Source: https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf Image Source: https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf

FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas. Source: FoodX-251: A Dataset for Fine-grained Food Classification Image Source: Kaur et al

The L-Bird (Large-Bird) dataset contains nearly 4.8 million images which are obtained by searching images of a total of 10,982 bird species from the Internet. Source: Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data Image Source: https://arxiv.org/pdf/1511.06789.pdf

The FeatherV1 dataset is a dataset for fine-grained visual classification. It contains 28,272 images of feathers categorized by 595 bird species. Source: https://github.com/feathers-dataset/feathersv1-dataset Image Source: https://github.com/feathers-dataset/feathersv1-dataset

Data for the paper entitled Quantifying yeast colony morphologies with feature engineering from time-lapse photography by A. Goldschmidt et al. (https://arxiv.org/abs/2201.05259) This project is a collaboration between Dudley Lab at the Pacific NW Research Institute and the J. Nathan Kutz group at the University of Washington. Summary: Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae.