Omkar2118/datasets huggingface dataset:
RegDB
Full name: Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)

RegDB is used for Visible-Infrared Re-ID which handles the cross-modality matching between the daytime visible and night-time infrared images. The dataset contains images of 412 people. It includes 10 color and 10 thermal images for each person.

Source: [Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras](https://www.mdpi.com/1424-8220/17/3/605)

Introducing paper: Deep Learning for Person Re-identification: A Survey and Outlook
Kennedy Space Center
Full name: Kennedy Space Center

**Kennedy Space Center** is a dataset for the classification of wetland vegetation at the Kennedy Space Center, Florida using hyperspectral imagery. Hyperspectral data were acquired over KSC on March 23, 1996 using JPL's Airborne Visible/Infrared Imaging Spectrometer.
The Pile
Full name: The Pile

The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.

Datasheet: [Datasheet for the Pile](https://paperswithcode.com/paper/datasheet-for-the-pile)

Introducing paper: The Pile: An 800GB Dataset of Diverse Text for Language Modeling
OC
Full name: Drowsiness-Detection

These images were generated using UnityEyes simulator, after including essential eyeball physiology elements and modeling binocular vision dynamics. The images are annotated with head pose and gaze direction information, besides 2D and 3D landmarks of eye's most important features. Additionally, the images are distributed into two classes denoting the status of the eye (Open for open eyes, Closed for closed eyes). This dataset was used to train a DNN model for detecting drowsiness status of a driver. The dataset contains 1,704 training images, 4,232 testing images and additional 4,103 images for improvements.

Introducing paper: Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
DSD100

The dsd100 is a dataset of 100 full lengths of music tracks of different styles along with their isolated drums, bass, vocals, and other stems.

dsd100 contains two folders, a folder with a training set: "train", composed of 50 songs, and a folder with a test set: "test", composed of 50 songs. Supervised approaches should be trained on the training set and tested on both sets.

For each file, the mixture corresponds to the sum of all the signals. All signals are stereophonic and encoded at 44.1kHz.
NASA Worldview
Full name: Understanding Clouds from Satellite Images

In this competition you will be identifying regions in satellite images that contain certain cloud formations, with label names: Fish, Flower, Gravel, Sugar. For each image in the test set, you must segment the regions of each cloud formation label. Each image has at least one cloud formation, and can possibly contain up to all all four.

The images were downloaded from NASA Worldview. Three regions, spanning 21 degrees longitude and 14 degrees latitude, were chosen. The true-color images were taken from two polar-orbiting satellites, TERRA and AQUA, each of which pass a specific region once a day. Due to the small footprint of the imager (MODIS) on board these satellites, an image might be stitched together from two orbits. The remaining area, which has not been covered by two succeeding orbits, is marked black.

The labels were created in a crowd-sourcing activity at the Max-Planck-Institite for Meteorology in Hamburg, Germany, and the Laboratoire de météorologie dynamique in Paris, France. A team of 68 scientists identified areas of cloud patterns in each image, and each images was labeled by approximately 3 different scientists. Ground truth was determined by the union of the areas marked by all labelers for that image, after removing any black band area from the areas.

The segment for each cloud formation label for an image is encoded into a single row, even if there are several non-contiguous areas of the same formation in an image. If there is no area of a certain cloud type for an image, the corresponding EncodedPixels prediction should be left blank. You can read more about the encoding standard on the Evaluation page.

Files
train.csv - the run length encoded segmentations for each image-label pair in the train_images
train_images.zip - folder of training images
test_images.zip - folder of test images; your task is to predict the segmentations masks of each of the 4 cloud types (labels) for each image. IMPORTANT: Your prediction masks should be scaled down to 350 x 525 px.
sample_submission.csv - a sample submission file in the correct format
CDD Dataset (season-varying)

Source: [CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS](https://pdfs.semanticscholar.org/ae15/e5ccccaaff44ab542003386349ef1d3b7511.pdf)

Introducing paper: CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS
ICT-3DHP

ICT-3DHP is collected using the Microsoft Kinect sensor and contains RGB images and depth maps of about 14k frames, divided in 10 sequences. The image resolution is 640 × 480 pixels. An hardware sensor (Polhemus Fastrack) is exploited to generate the ground truth annotation. The device is placed on a white cap worn by each subject, visible in both RGB and depth frames.

Source: [POSEidon: Face-from-Depth for Driver Pose Estimation](https://arxiv.org/pdf/1611.10195v3.pdf)
MemeTracker
Full name: MemeTracker

The Memetracker corpus contains articles from mainstream media and blogs from August 1 to October 31, 2008 with about 1 million documents per day. It has 10,967 hyperlink cascades among 600 media sites.

Source: [Marked Temporal Dynamics Modeling based on Recurrent Neural Network](https://arxiv.org/abs/1701.03918)
Image Source: [http://blog.fabric.ch/index.php?/archives/292-Memetracker-Tracking-News-Phrases-over-the-Web.html](http://blog.fabric.ch/index.php?/archives/292-Memetracker-Tracking-News-Phrases-over-the-Web.html)

Introducing paper: Meme-tracking and the dynamics of the news cycle
ETH SfM
Full name: ETH Structure-from-Motion

The **ETH SfM** (structure-from-motion) dataset is a dataset for 3D Reconstruction. The benchmark investigates how different methods perform in terms of building a 3D model from a set of available 2D images.

Source: [SOSNet: Second Order Similarity Regularization forLocal Descriptor Learning](https://arxiv.org/abs/1904.05019)
Image Source: [https://cvg.ethz.ch/research/symmetries-in-sfm/](https://cvg.ethz.ch/research/symmetries-in-sfm/)

Introducing paper: Comparative Evaluation of Hand-Crafted and Learned Local Features
INRIA-Horse
Full name: INRIA-Horse

The **INRIA-Horse** dataset consists of 170 horse images and 170 images without horses. All horses in all images are annotated with a bounding-box. The main challenges it offers are clutter, intra-class shape variability, and scale changes. The horses are mostly unoccluded, taken from approximately the side viewpoint, and face the same direction.

Source: [Dynamical And-Or Graph Learning for Object Shape Modeling and Detection](https://arxiv.org/abs/1502.00741)
Image Source: [http://calvin-vision.net/datasets/inria-horses/](http://calvin-vision.net/datasets/inria-horses/)

Introducing paper: Scale-Invariant Shape Features for Recognition of Object Categories
Office-Caltech-10
Full name: Office-Caltech-10

**Office-Caltech-10** a standard benchmark for domain adaptation, which consists of Office 10 and Caltech 10 datasets. It contains the 10 overlapping categories between the Office dataset and Caltech256 dataset. SURF BoW historgram features, vector quantized to 800 dimensions are also available for this dataset.

Source: [Impact of ImageNet Model Selection on Domain Adaptation](https://arxiv.org/abs/2002.02559)
Image Source: [https://arxiv.org/abs/1409.5241](https://arxiv.org/abs/1409.5241)
Poser
Full name: Poser

The **Poser** dataset is a dataset for pose estimation which consists of 1927 training and 418 test images. These images are synthetically generated and tuned to unimodal predictions. The images were generated using the Poser software package.

Source: [Overlapping Cover Local Regression Machines](https://arxiv.org/abs/1701.01218)
Image Source: [https://www.researchgate.net/figure/Test-data-used-in-the-user-study-Left-the-pose-pictures-shown-to-the-user-Middle-the_fig17_221847487](https://www.researchgate.net/figure/Test-data-used-in-the-user-study-Left-the-pose-pictures-shown-to-the-user-Middle-the_fig17_221847487)

Introducing paper: Recovering 3D Human Pose from Monocular Images
UKP
Full name: UKP Argument Annotated Essays

The **UKP** Argument Annotated Essays corpus consists of argument annotated persuasive essays including annotations of argument components and argumentative relations.

Source: [https://www.informatik.tu-darmstadt.de/ukp/research_6/data/argumentation_mining_1/argument_annotated_essays/index.en.jsp](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/argumentation_mining_1/argument_annotated_essays/index.en.jsp)
Image Source: [https://www.aclweb.org/anthology/C14-1142.pdf](https://www.aclweb.org/anthology/C14-1142.pdf)

Introducing paper: Annotating Argument Components and Relations in Persuasive Essays
ETH BIWI Walking Pedestrians
Full name: ETH BIWI Walking Pedestrians

The BIWI Walking Pedestrians dataset consists of walking pedestrians in busy scenarios from a birds eye view.

Source: [https://icu.ee.ethz.ch/research/datsets.html](https://icu.ee.ethz.ch/research/datsets.html)
Image Source: [https://icu.ee.ethz.ch/research/datsets.html](https://icu.ee.ethz.ch/research/datsets.html)

Introducing paper: You'll never walk alone: Modeling social behavior for multi-target tracking
WASABI
Full name: WASABI

The **WASABI** Song Corpus is a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis.
More specifically, given that lyrics encode an important part of the semantics of a song, the authors focus on the description of the methods they proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed.
The corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. Such corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation recommendation of songs.

Source: [https://github.com/micbuffa/WasabiDataset](https://github.com/micbuffa/WasabiDataset)
Image Source: [https://arxiv.org/abs/1912.02477](https://arxiv.org/abs/1912.02477)
Multilingual Reuters
Full name: Multilingual Reuters Collection

The **Multilingual Reuters** Collection dataset comprises over 11,000 articles from six classes in five languages, i.e., English (E), French (F), German (G), Italian (I), and Spanish (S).

Source: [Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network](https://arxiv.org/abs/2008.02714)
Image Source: [https://papers.nips.cc/paper/2009/file/f79921bbae40a577928b76d2fc3edc2a-Paper.pdf](https://papers.nips.cc/paper/2009/file/f79921bbae40a577928b76d2fc3edc2a-Paper.pdf)

Introducing paper: Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization
Pan+ChiPhoto
Full name: Pan+ChiPhoto

**Pan+ChiPhoto** dataset is a Chinese character dataset. It is built by the combination of two datasets: ChiPhoto and Pan_Chinese_Character dataset. The images in this dataset are mainly captured at outdoors in Beijing and Shanghai, China, which involve various scenes like signs, boards, advertisements, banners, objects with texts printed on their surfaces.

Source: [Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs](https://arxiv.org/abs/1907.05577)
Image Source: [https://www.researchgate.net/publication/318679069_Multi-order_Co-occurrence_Activations_Encoded_with_Fisher_Vector_for_Scene_Character_Recognition](https://www.researchgate.net/publication/318679069_Multi-order_Co-occurrence_Activations_Encoded_with_Fisher_Vector_for_Scene_Character_Recognition)

Introducing paper: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients
ISI_Bengali_Character
Full name: ISI_Bengali_Character

The **ISI_Bengali_Character** dataset contains 158 classes of Bengali numerals, characters or their parts. 19,530 Bengali character samples are available. Most of the images in the dataset are synthesized.

Source: [Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs](https://arxiv.org/abs/1907.05577)
Image Source: [https://www.isical.ac.in/~ujjwal/download/SegmentedSceneCharacter.html](https://www.isical.ac.in/~ujjwal/download/SegmentedSceneCharacter.html)

Introducing paper: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients
Florentine
Full name: Florentine

The **Florentine** dataset is a dataset of facial gestures which contains facial clips from 160 subjects (both male and female), where gestures were artificially generated according to a specific request, or genuinely given due to a shown stimulus. 1032 clips were captured for posed expressions and 1745 clips for induced facial expressions amounting to a total of 2777 video clips. Genuine facial expressions were induced in subjects using visual stimuli, i.e. videos selected randomly from a bank of Youtube videos to generate a specific emotion.

Source: [Deep video gesture recognition using illumination invariants](https://arxiv.org/abs/1603.06531)
Image Source: [https://www.micc.unifi.it/resources/datasets/florence-3d-faces/](https://www.micc.unifi.it/resources/datasets/florence-3d-faces/)

Introducing paper: Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild
INRIA DLFD
Full name: INRIA Dense Light Field

The **INRIA Dense Light Field** Dataset (DLFD) is a dataset for testing depth estimation methods in a light field. DLFD contains 39 scenes with disparity range [-4,4] pixels. The light fields are of spatial resolution 512 x 512 and angular resolution 9 x 9.

Source: [http://clim.inria.fr/Datasets/InriaSynLF/index.html](http://clim.inria.fr/Datasets/InriaSynLF/index.html)
Image Source: [http://clim.inria.fr/Datasets/InriaSynLF/index.html](http://clim.inria.fr/Datasets/InriaSynLF/index.html)

Introducing paper: A Framework for Learning Depth From a Flexible Subset of Dense and Sparse Light Field Views
INRIA SLFD
Full name: INRIA Sparse Light Field

The INRIA Sprse Light Field Dataset (SLFD) is a dataset for testing depth estimation methods in a light field. SLFD contains 53 scenes with disparity range [-20,20] pixels. The light fields are of spatial resolution 512 x 512 and angular resolution 9 x 9.

Source: [http://clim.inria.fr/Datasets/InriaSynLF/index.html](http://clim.inria.fr/Datasets/InriaSynLF/index.html)
Image Source: [http://clim.inria.fr/Datasets/InriaSynLF/index.html](http://clim.inria.fr/Datasets/InriaSynLF/index.html)

Introducing paper: A Framework for Learning Depth From a Flexible Subset of Dense and Sparse Light Field Views
AIDS Antiviral Screen
Full name: AIDS Antiviral Screen

The **AIDS Antiviral Screen** dataset is a dataset of screens checking tens of thousands of compounds for evidence of anti-HIV activity. The available screen results are chemical graph-structured data of these various compounds.

Source: [Graph Neural Processes Towards Bayesian Graph Neural Networks](https://arxiv.org/abs/1902.10042)

Introducing paper: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
Retinal Microsurgery
Full name: Retinal Microsurgery

The **Retinal Microsurgery** dataset is a dataset for surgical instrument tracking. It consists of 18 in-vivo sequences, each with 200 frames of resolution 1920 × 1080 pixels. The dataset is further classified into four instrument-dependent subsets. The annotated tool joints are n=3 and semantic classes c=2 (tool and background).

Source: [Concurrent Segmentation and Localization for Tracking of Surgical Instruments](https://arxiv.org/abs/1703.10701)
Image Source: [https://sites.google.com/site/sznitr/research/retinalmicrosurgery](https://sites.google.com/site/sznitr/research/retinalmicrosurgery)

Introducing paper: Real-time localization of articulated surgical instruments in retinal microsurgery
Daimler Monocular Pedestrian Detection
Full name: Daimler Monocular Pedestrian Detection

The **Daimler Monocular Pedestrian Detection** dataset is a dataset for pedestrian detection in urban environments. The training set contains 15560 pedestrian samples (image cut-outs at 48×96 resolution) and 6744 additional full images without pedestrians for extracting negative samples. The test set contains an independent sequence with more than 21790 images and 56492 pedestrian labels (fully visible or partially occluded), captured from a vehicle during a 27 min driving through the urban traffic.

Source: [A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis](https://arxiv.org/abs/1904.11784)
Image Source: [http://www.gavrila.net/Datasets/Daimler_Pedestrian_Benchmark_D/Daimler_Mono_Ped__Detection_Be/daimler_mono_ped__detection_be.html](http://www.gavrila.net/Datasets/Daimler_Pedestrian_Benchmark_D/Daimler_Mono_Ped__Detection_Be/daimler_mono_ped__detection_be.html)
ETHZ-Shape
Full name: ETHZ-Shape

The ETHZ Shape dataset contains images of five diverse shape-based classes, collected from Flickr and Google Images. The main challenges it offers are clutter, intra-class shape variability, and scale changes. The authors deliberately selected several images where the object comprises only a rather small portion of the image, and made an effort to include objects appearing at a wide range of scales. The objects are mostly unoccluded and are all taken from approximately the same viewpoint (the side).

Source: [http://calvin-vision.net/datasets/ethz-shape-classes/](http://calvin-vision.net/datasets/ethz-shape-classes/)
Image Source: [http://calvin-vision.net/datasets/ethz-shape-classes/](http://calvin-vision.net/datasets/ethz-shape-classes/)

Introducing paper: Groups of Adjacent Contour Segments for Object Detection
L-Bird
Full name: Large-Bird

The **L-Bird** (**Large-Bird**) dataset contains nearly 4.8 million images which are obtained by searching images of a total of 10,982 bird species from the Internet.

Source: [Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data](https://arxiv.org/abs/1807.10916)
Image Source: [https://arxiv.org/pdf/1511.06789.pdf](https://arxiv.org/pdf/1511.06789.pdf)

Introducing paper: The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Extended BBC Pose
Full name: Extended BBC Pose

**Extended BBC Pose** is a pose estimation dataset which extends the BBC Pose dataset with 72 additional training videos. Combined with the original BBC TV dataset, the dataset contains 92 videos (82 training, 5 validation and 5 testing), i.e. around 7 million frames. The frames of the new 72 videos are automatically assigned joint locations (used as ground truth for training) with the tracker of Charles et al. IJCV'13.

Source: [https://www.robots.ox.ac.uk/~vgg/data/pose/](https://www.robots.ox.ac.uk/~vgg/data/pose/)
Image Source: [https://www.robots.ox.ac.uk/~vgg/data/pose/](https://www.robots.ox.ac.uk/~vgg/data/pose/)
Short BBC Pose
Full name: Short BBC Pose

**Short BBC Pose** contains five one-hour-long videos with sign language signers each with different sleeve length (in contrast to the BBC pose and Extended BBC Pose, which only contain signers with moderately long sleeves). Each of the five videos has 200 test frames (which have been manually annotated with joint locations), amounting to 1,000 test frames in total. Test frames were selected by the authors to contain a diverse range of poses.

Source: [https://www.robots.ox.ac.uk/~vgg/data/pose/index.html#citation](https://www.robots.ox.ac.uk/~vgg/data/pose/index.html#citation)
Image Source: [https://www.robots.ox.ac.uk/~vgg/publications/2013/Charles13/charles13.pdf](https://www.robots.ox.ac.uk/~vgg/publications/2013/Charles13/charles13.pdf)
ChaLearn Pose
Full name: ChaLearn Pose

**ChaLearn Pose** is a subset of the ChaLearn 2013 Multi-modal gesture dataset from Escalera et al. ICMI'13, which contains 23 hours of Kinect data of 27 persons performing 20 Italian gestures. The data includes RGB, depth, foreground segmentations and full body skeletons. In this dataset, both the training and testing labels are noisy (from Kinect).

Source: [https://www.robots.ox.ac.uk/~vgg/data/pose/index.html#citation](https://www.robots.ox.ac.uk/~vgg/data/pose/index.html#citation)
Image Source: [http://sunai.uoc.edu/chalearnLAP/](http://sunai.uoc.edu/chalearnLAP/)

Introducing paper: Multi-modal Gesture Recognition Challenge 2013: Dataset and Results