Omkar2118/datasets huggingface dataset:
GraspNet-1Billion

**GraspNet-1Billion** provides large-scale training data and a standard evaluation platform for the task of general robotic grasping. The dataset contains 97,280 RGB-D image with over one billion grasp poses.

Introducing paper: GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
MAEC
Full name: Multimodal Aligned Earnings Conference Call Dataset

**MAEC** is a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. 

Source: [MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction](https://dl.acm.org/doi/10.1145/3340531.3412879)
NuCLS
Full name: Nucleus Classification, Localization and Segmentation

The NuCLS dataset contains over 220,000 labeled nuclei from breast cancer images from TCGA. These nuclei were annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. These data can be used in several ways to develop and validate algorithms for nuclear detection, classification, and segmentation, or as a resource to develop and evaluate methods for interrater analysis.

Data from both single-rater and multi-rater studies are provided. For single-rater data we provide both pathologist-reviewed and uncorrected annotations. For multi-rater datasets we provide annotations generated with and without suggestions from weak segmentation and classification algorithms.

Source: [Amgad et al.](https://arxiv.org/pdf/2102.09099v1.pdf)

Image source:  [Amgad et al.](https://arxiv.org/pdf/2102.09099v1.pdf)

Introducing paper: NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
K-Hairstyle

K-hairstyle is a novel large-scale Korean hairstyle dataset with 256,679 high-resolution images. In addition, K-hairstyle contains various hair attributes annotated by Korean expert hair stylists and hair segmentation masks.

Source: [K-Hairstyle: A Large-scale Korean hairstyle dataset for virtual hair editing and hairstyle classification](https://paperswithcode.com/paper/k-hairstyle-a-large-scale-korean-hairstyle)

Image source: [Kim et al.](https://arxiv.org/pdf/2102.06288v1.pdf)

Introducing paper: K-Hairstyle: A Large-scale Korean Hairstyle Dataset for Virtual Hair Editing and Hairstyle Classification
CC12M
Full name: Conceptual 12M

Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training.

Source: [Changpinyo et al.](https://arxiv.org/pdf/2102.08981v1.pdf)

Image source: [Changpinyo et al.](https://arxiv.org/pdf/2102.08981v1.pdf)

Introducing paper: Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Brain US

This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old) who were treated between 2010 and 2016. The dataset contained subjects with IVH and without (healthy subjects but in risk of developing IVH). The US scans were collected using a Philips US machine with a C8-5 broadband curved array transducer using coronal and sagittal scan planes. For every collected image ventricles and septum pellecudi are manually segmented by an expert ultrasonographer. We split these images randomly into 1300 Training images and 329 Testing images for experiments. Note that these images are of size 512 × 512. 

Source: [Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data](https://arxiv.org/pdf/1912.08364.pdf)

Image source: [Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data](https://arxiv.org/pdf/1912.08364.pdf)

Introducing paper: Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
AbstRCT - Neoplasm

The AbstRCT dataset consists of randomized controlled trials retrieved from the MEDLINE database via PubMed search. The trials are annotated with argument components and argumentative relations.

Paper: [Transformer-Based Argument Mining for Healthcare Applications](https://hal.archives-ouvertes.fr/hal-02879293/)
CDCP
Full name: Cornell eRulemaking Corpus

The Cornell eRulemaking Corpus – CDCP is an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary
for readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.

Introducing paper: A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments
DRI Corpus
Full name: Dr. Inventor Multi-layer Scientific Corpus

The **Dr. Inventor Multi-Layer Scientific Corpus** (**DRI Corpus**) includes 40 Computer Graphics papers, selected by domain experts. Each paper of the Corpus has been annotated by three annotators by providing the following layers of annotations, each one characterizing a core aspect of scientific publications:

* Scientific discourse: each sentence has been associated to a specific scientific discourse category (Background, Approach, Challenge, Future Work, etc.).
* Subjective statements and novelty: each sentence has been characterized with respect to advantages, disadvantages and novel aspects presented.
* Citation purpose: to each citation has been associated a purpose specifying the reason why the authors of the paper cited the specific piece of research.
* Summary relevance of sentences and hand written summaries: each sentence of the paper has been characterized by an integer score ranging from 1 to 5, to point out the relevance of the same sentence for its inclusion in the summary of the paper. Sentences rated as 5 are the most relevant ones to summarize a paper. For each paper three hand-written summaries (max 250 words) are provided.

Source: [Dr. Inventor Multi-layer Scientific Corpus](http://sempub.taln.upf.edu/dricorpus)

Introducing paper: A Multi-Layered Annotated Corpus of Scientific Papers
PIPAL
Full name: Perceptual Image Processing ALgorithms IQA Dataset

PIPAL training set contains 200 reference images, 40 distortion types, 23k distortion images, and more than one million human ratings. Especially, we include GAN-based algorithms’ outputs as a new GAN-based distortion type. We employ the Elo rating system to assign the Mean Opinion Scores (MOS).

Introducing paper: PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration
ReCAM
Full name: SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

Tasks
Our shared task has three subtasks. Subtask 1 and 2 focus on evaluating machine learning models' performance with regard to two definitions of abstractness (Spreen and Schulz, 1966; Changizi, 2008), which we call imperceptibility and nonspecificity, respectively. Subtask 3 aims to provide some insights to their relationships.

• Subtask 1: ReCAM-Imperceptibility

Concrete words refer to things, events, and properties that we can perceive directly with our senses (Spreen and Schulz, 1966; Coltheart 1981; Turney et al., 2011), e.g., donut, trees, and red. In contrast, abstract words refer to ideas and concepts that are distant from immediate perception. Examples include objective, culture, and economy. In subtask 1, the participanting systems are required to perform reading comprehension of abstract meaning for imperceptible concepts.

Below is an example. Given a passage and a question, your model needs to choose from the five candidates the best one for replacing @placeholder.



 

• Subtask 2: ReCAM-Nonspecificity
 
Subtask 2 focuses on a different type of definition. Compared to concrete concepts like groundhog and whale, hypernyms such as vertebrate are regarded as more abstract (Changizi, 2008). 
 
• Subtask 3: ReCAM-Intersection
Subtask 3 aims to provide more insights to the relationship of the two views on abstractness, In this subtask, we test the performance of a system that is trained on one definition and evaluted on the other.

Introducing paper: SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning