Omkar2118/datasets huggingface dataset:
USIS10K
Full name: Large-scale Underwater Salient Instance Segmentation Dataset

We construct the first large-scale dataset, USIS10K, for the underwater salient instance segmentation task, which contains 10,632 images and pixel-level annotations of 7 categories. As far as we know, this is the largest salient instance segmentation dataset, and includes Class-Agnostic and Multi-Class labels simultaneously.

If you use this dataset in your work, please consider to cite:

@inproceedings{
  lian2024icml,
  title={Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset},
  author={Shijie Lian and Ziyi Zhang and Hua Li and Wenjie Li and Laurence Tianruo Yang and Sam Kwong and Runmin Cong},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024},
  url={https://openreview.net/forum?id=snhurpZt63}
}

Introducing paper: Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
The ULS23 Challenge Test Set

The ULS23 test set contains 725 lesions from 284 patients of the Radboudumc and JBZ hospitals in the Netherlands. It is intended to be used to measure the performance of 3D universal lesion segmentation models for Computed Tomography (CT). To prepare the data, radiological reports from both participating institutions where searched using NLP tools identifying patients with measurable target lesions, indicating that these lesions were clinically relevant. A random sample of patients was selected, 56.3% of which were male and with diverse scanner manufacturers. The lesions were annotated in 3D by expert radiologists with over 10 years of experience in reading oncological scans. ULS23 is an open benchmark, and we invite ongoing submissions to advance the development of future ULS models.

Introducing paper: The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
COAT
Full name: CommonSense Object Affordance Task

Useful for checking the physical reasoning capabilities in household agents. Made through human annotations of what type of object configurations we prefer for accomplishing a household task.

Consists of 4 major datasets that are organized for evaluating such object selection capabilities across 3 major factors 1) Utility [1 dataset] 2) Context [1 dataset] 3) Physical State [2 datasets] 

We also provide 2 datasets used for evaluating object selection capabilities by applying all 3 factors together. Namely the F_{ideal} and F_{moderate} datasets.

Introducing paper: Physical Reasoning and Object Planning for Household Embodied Agents
AnoVox

AnoVox is a large-scale benchmark for ANOmaly detection in autonomous driving. AnoVox incorporates multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. AnoVox contains both content and temporal anomalies.

Introducing paper: AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
GraphInstruct

The GraphInstruct dataset is part of a benchmark proposed in the paper titled "GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability." This benchmark is designed to evaluate and enhance the graph understanding abilities of large language models (LLMs). It includes **21 classical graph reasoning tasks**, providing diverse graph generation pipelines and detailed reasoning steps².

The dataset is used to construct models like GraphLM and GraphLM+, which are trained through efficient instruction-tuning and a step mask training strategy to show prominent graph understanding capability. These models have demonstrated superiority over other LLMs in understanding and reasoning with graph data².

(1) GraphInstruct: Empowering Large Language Models with Graph .... https://arxiv.org/abs/2403.04483.
(2) CGCL-codes/GraphInstruct - GitHub. https://github.com/CGCL-codes/GraphInstruct.
(3) GraphWiz/GraphInstruct · Datasets at Hugging Face. https://huggingface.co/datasets/GraphWiz/GraphInstruct/viewer.
(4) GraphWiz: An Instruction-Following Language Model for Graph Problems. https://arxiv.org/abs/2402.16029.
(5) undefined. https://doi.org/10.48550/arXiv.2403.04483.

Introducing paper: GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability