Omkar2118/datasets huggingface dataset:
WORD
Full name: Whole abdominal  Organs Dataset

**WORD** is a dataset for organ semantic segmentation that contains 150 abdominal CT volumes (30,495 slices) and each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotation, which may be the largest dataset with whole abdominal organs annotation.

Introducing paper: WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
REAL-M

**Real-M** is a crowd-sourced speech-separation corpus of real-life mixtures. The mixtures are recorded in different acoustic environments using a wide variety of recording devices such as laptops and smartphones, thus reflecting more closely potential application scenarios.

Introducing paper: REAL-M: Towards Speech Separation on Real Mixtures
RIKEN Microstructural Imaging Metadatabase

The **RIKEN Microstructural Imaging Metadatabase** is a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level.

Introducing paper: Development of Semantic Web-based Imaging Database for Biological Morphome
AdobeVFR syn
Full name: Adobe Visual Font Recognition synthetic dataset

Subset of AdobeVFR. The dataset contains images depicting English text and consists of 1000 synthetic images for training and 100 for testing, for each of 2383 font classes. The training and test sets are called *VFR_syn_train* and *VFR_syn_val*, respectively.

The other part of AdobeVFR consists of "real-world text images".

Introducing paper: DeepFont: Identify Your Font from An Image
Explor_all
Full name: Explor_all

Explor_all font image dataset https://drive.google.com/file/d/1P2DbNbVw4Q__WcV1YdzE7zsDKilmd3pO/view
SDSS Galaxies
Full name: SDSS galaxies as imaged by DESI

This is a dataset of 306,006 galaxies whose coordinates are taken from the 
Sloan Digital Sky Survey Data Release 7 and a modified catalogue from 
Brinchmann+2003 and Wilman+2010. This volume complete sample has 
an r-band absolute magnitude limit of $M_r\leq-20$ and a redshift limit of 
$z\leq0.08$. See Arora+2019 for details.

This catalogue covers a wide range of environments from clusters to groups 
and field systems. The galaxy images are taken from the Dark Energy 
Spectroscopic Instrument, and contain $g$, $r$, and $z$ bands.

Introducing paper: Realistic galaxy image simulation via score-based generative models
VFR-447

A synthetic dataset containing 447 typefaces with only one font variation for each typeface, created for visual font recognition.

> Each class in VFR-447 and VFR-2420 has 1,000 synthetic word images, which are evenly split into 500 training and 500 testing. There are no common words between the training and testing images.

> To model the realistic use cases, we add moderate distortions and noise to the synthetic data.

Introducing paper: Large-Scale Visual Font Recognition
VFR-2420

A synthetic dataset containing word images of 447 typefaces with font variations for each typeface, created for visual font recognition.

> We collect in total 447 typefaces, each with different number of variations resulting from combinations of different styles, e.g., regular, semibold, bold, black, and italic, leading to 2,420 font classes in the end.

> Each class in VFR-447 and VFR-2420 has 1,000 synthetic word images, which are evenly split into 500 training and 500 testing. There are no common words between the training and testing images.

> To model the realistic use cases, we add moderate distortions and noise to the synthetic data.

Introducing paper: Large-Scale Visual Font Recognition
VFR-Wild

325 word images intended for font recognition, whose fonts are included in [VFR-447] (and [VFR-2420]).

> (...) 325 real world test images for the font classes we have in the training set. These images were collected from typography forums, such as myfonts.com, where people post these images seeking help from experts to identify the fonts. Compared with the synthetic data, these images typically have much larger appearance variations caused by scale, background, lighting, noise, perspective distortions, and compression artifacts. We manually cropped the texts from these images with a bounding box to normalize the text size approximately to the same scale as the synthetic data.

[VFR-447]: https://paperswithcode.com/dataset/vfr-447
[VFR-2420]: https://paperswithcode.com/dataset/vfr-2420

Introducing paper: Large-Scale Visual Font Recognition
AdobeVFR real
Full name: Adobe Visual Font Recognition real-world images dataset

Subset of AdobeVFR. The dataset contains "real-world text images".

> We collected 201,780 text images from various typography forums, where people post these images seeking help from experts to identify the fonts. Most of them come with hand-annotated font labels which may be inaccurate. (...) Finally, we obtain 4,384 real-world test images with reliable labels, covering 617 classes (out of 2,383). (...) Removing the 4,384 labeled images from the full set, we are left with 197,396 unlabeled real- world images which we denote as *VFR_real_u*.

The labeled images form *VFR_real_test*. The other part of AdobeVFR consists of synthetic data (with 2383 classes).

Introducing paper: DeepFont: Identify Your Font from An Image