datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
plate_effects
Multi-Source Domain Adaptation for Bioimaging Data (MSCDA-BioIm)
MSCDA-BioIm is a biomedical microscopy benchmark for evaluating test-time and in-context domain adaptation under realistic batch effects. Built from the large-scale JUMP-CP dataset, it targets mechanism-of-action (MoA) classification using five-channel images of compounds associated with eight well-defined MoA classes. The dataset is organized by experimental batches and imaging… See the full description on the dataset page: https://huggingface.co/datasets/anasanchezf/plate_effects.red-eye-effectAction-EffectDespite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.Synset-Background-Effect-Datasets
Synset Background Effect Datasets
For investigating the effect of background on feature importance and classification performance, we systematically generated six synthetic datasets for the
task of traffic sign recognition, which differ only in their degree of camera variation and background correlation. Each of these datasets contains 82 classes
of traffic signs with 1,100 images per class, resulting in 90,200 images per dataset, summing up to a total of 541,200 images.… See the full description on the dataset page: https://huggingface.co/datasets/FraunhoferIOSB/Synset-Background-Effect-Datasets.red-eye-effect