Omkar2118/datasets huggingface dataset:
Darmstadt Noise Dataset
Full name: zaid allal

the dataset contains data about hydrogen storage in metal hydrides
Multi-Ego

A new multi-view egocentric dataset, Multi-Ego. The dataset is recorded simultaneously by three cameras, covering a wide variety of real-life scenarios. The footage is annotated by multiple individuals under various summarization configurations, with a consensus analysis ensuring a reliable ground truth.

Source: [Multi-Stream Dynamic Video Summarization](/paper/multi-view-egocentric-video-summarization)

Introducing paper: Multi-Stream Dynamic Video Summarization
Reddit TIFU

**Reddit TIFU** dataset is a newly collected Reddit dataset, where TIFU denotes the name of /r/tifu subbreddit.
There are 122,933 text-summary pairs in total.

Source: [https://github.com/ctr4si/MMN](https://github.com/ctr4si/MMN)

Introducing paper: Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
FBMS-59
Full name: Freiburg-Berkeley Motion Segmentation

The **Freiburg-Berkeley Motion Segmentation** Dataset (**FBMS-59**) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets.

Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html)
Image Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html)
CIFAR-10
Full name: CIFAR-10

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Introducing paper: Learning multiple layers of features from tiny images
CIFAR-100

The **CIFAR-100** dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class.

The criteria for deciding whether an image belongs to a class were as follows:

* The class name should be high on the list of likely answers to the question “What is in this picture?”
* The image should be photo-realistic. Labelers were instructed to reject line drawings.
* The image should contain only one prominent instance of the object to which the class refers.
* The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler.

Source: [https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)
Image Source: [https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)

Introducing paper: Learning multiple layers of features from tiny images
ImageNet VID

ImageNet VID is a large-scale public dataset
for video object detection and contains more than 1M frames for training and
more than 100k frames for validation.
SK-LARGE
Full name: SK-LARGE

**SK-LARGE** is a benchmark dataset for object skeleton detection, built on the MS COCO dataset. It contains 1491 images, 746 for training and 745 for testing.

Source: [DeepFlux for Skeletons in the Wild](https://arxiv.org/abs/1811.12608)
Image Source: [http://kaizhao.net/sk-large](http://kaizhao.net/sk-large)

Introducing paper: DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
Pavia University
Full name: Pavia University

The **Pavia University** dataset is a hyperspectral image dataset which gathered by a sensor known as the reflective optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy. The image consists of 610×340 pixels with 115 spectral bands. The image is divided into 9 classes with a total of 42,776 labelled samples, including the asphalt, meadows, gravel, trees, metal sheet, bare soil, bitumen, brick, and shadow.

Source: [Diversity in Machine Learning](https://arxiv.org/abs/1807.01477)
Image Source: [http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University)

Introducing paper: Pavia centre and university