Omkar2118/datasets huggingface dataset:
RealEstate10K
Full name: RealEstate10K

**RealEstate10K** is a large dataset of camera poses corresponding to 10 million frames derived from about 80,000 video clips, gathered from about 10,000 YouTube videos. For each clip, the poses form a trajectory where each pose specifies the camera position and orientation along the trajectory. These poses are derived by running SLAM and bundle adjustment algorithms on a large set of videos.
Wikipedia Generation
Full name: Wikipedia Generation

**Wikipedia Generation** is a dataset for article generation from Wikipedia from references at the end of Wikipedia page and the top 10 search results for the Wikipedia topic.

Introducing paper: Generating Wikipedia by Summarizing Long Sequences
WildestFaces

WildestFaces is tailored to study cross-domain recognition under a variety of adverse conditions. 

Source: [Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos](https://arxiv.org/pdf/2009.07576)

Introducing paper: Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos
FAD
Full name: Face Attributes Dataset

FAD is a dataset that have roughly 200,000 attribute labels for the above traits, for over 10,000 facial images.

Source: [Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information](https://arxiv.org/pdf/1605.09062)

Introducing paper: Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
VQA 360°

VQA 360° is a dataset for visual question answering on 360° images containing around 17,000 real-world image-question-answer triplets for a variety of question types. 

Source: [Visual Question Answering on 360° Images](http://aliensunmin.github.io/project/360-VQA/)

Introducing paper: Visual Question Answering on 360° Images
PHSPD
Full name: Polarization Human Shape and Pose Dataset

PHSPD is a home-grown polarization image dataset of various human shapes and poses.

Source: [Polarization Human Shape and Pose Dataset](https://arxiv.org/pdf/2004.14899)

Introducing paper: Polarization Human Shape and Pose Dataset
HARRISON

HARRISON dataset is a benchmark on hashtag recommendation for real world images in social networks. The HARRISON dataset is a realistic dataset, composed of 57,383 photos from Instagram and an average of 4.5 associated hashtags for each photo.

Source: [HARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks](https://arxiv.org/pdf/1605.05054)

Introducing paper: HARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks
FSOD
Full name: Few-Shot Object Detection Dataset

Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories.

Source: [FSOD](https://github.com/fanq15/Few-Shot-Object-Detection-Dataset)

Introducing paper: Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
DUS
Full name: Daimler Urban Segmentation

The **Daimler Urban Segmentation Dataset** is a dataset for semantic segmentation. It consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
HumanAct12

**HumanAct12** is a new 3D human motion dataset adopted from the polar image and 3D pose dataset PHSPD, with proper temporal cropping and action annotating. Statistically, there are 1191 3D motion clips(and 90,099 poses in total) which are categorized into 12 action classes, and 34 fine-grained sub-classes. The action types includes daily actions such as walk, run, sit down, jump up, warm up, etc. Fine-grained action types contain more specific information like Warm up by bowing left side, Warm up by pressing left leg, etc. 

Source: [Action2Motion: Conditioned Generation of 3D Human Motions](https://ericguo5513.github.io/action-to-motion/)

Introducing paper: Action2Motion: Conditioned Generation of 3D Human Motions
Large Age-Gap
Full name: Large Age-Gap

**Large Age-Gap (LAG)** is a dataset for face verification, The dataset contains 3,828 images of 1,010 celebrities. For each identity at least one child/young image and one adult/old image are present.

Introducing paper: Large age-gap face verification by feature injection in deep networks
Interestingness

The **Interestingness** dataset contains movie excerpts and key-frames and corresponding ground truth files based on classification into interesting and non-interesting samples. It is used for multimedia content interestingness classification. The dataset is composed of:

- Shots and key-frames from a set of 78 Hollywood-like movie trailers of different genres
- The corresponding ground truth
- Additional low-level and mid-level features
AKCES-GEC

AKCES-GEC is a new dataset on grammatical error correction for Czech.

Source: [Grammatical Error Correction in Low-Resource Scenarios](https://arxiv.org/pdf/1910.00353)

Introducing paper: Grammatical Error Correction in Low-Resource Scenarios
LASIESTA
Full name: LASIESTA

**LASIESTA (Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms)** is a segmentation and detection dataset composed by many real indoor and outdoor sequences organized into categories, each of one covering a specific challenge in moving object detection strategies.
E-GMD
Full name: Expanded Groove MIDI Dataset

Expanded Groove MIDI dataset (E-GMD) is an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations.

Source: [Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset](https://arxiv.org/pdf/2004.00188)

Introducing paper: Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset
FIRE
Full name: Fundus Image Registration Dataset

**Fundus Image Registration Dataset (FIRE)** is a dataset consisting of 129 retinal images forming 134 image pairs. These image pairs are split into 3 different categories depending on their characteristics. The images were acquired with a Nidek AFC-210 fundus camera, which acquires images with a resolution of 2912x2912 pixels and a FOV of 45° both in the x and y dimensions. Images were acquired at the Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki from 39 patients.
MVB
Full name: Multi View Baggage

MVB (Multi View Baggage) is a dataset for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other.

Source: [MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks](https://arxiv.org/pdf/1907.11366)

Introducing paper: MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
300-VW
Full name: 300 Videos in the Wild

**300 Videos in the Wild (300-VW)** is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each).
Imp1k

Imp1k is a new dataset of designs annotated with importance information.

Source: [Predicting Visual Importance Across Graphic Design Types](https://arxiv.org/pdf/2008.02912)

Introducing paper: Predicting Visual Importance Across Graphic Design Types
PIROPO
Full name: PIROPO

The **PIROPO database** (People in Indoor ROoms with Perspective and Omnidirectional cameras) comprises multiple sequences recorded in two different indoor rooms, using both omnidirectional and perspective cameras. The sequences contain people in a variety of situations, including people walking, standing, and sitting. Both annotated and non-annotated sequences are provided, where ground truth is point-based (each person in the scene is represented by the point located in the center of its head). In total, more than 100,000 annotated frames are available.
Million-AID

Million-AID is a large-scale benchmark dataset containing a million instances for RS scene classification. There are 51 semantic scene categories in Million-AID. And the scene categories are customized to match the land-use classification standards, which greatly enhance the practicability of the constructed Million-AID. Different form the existing scene classification datasets of which categories are organized with parallel or uncertain relationships, scene categories in Million-AID are organized with systematic relationship architecture, giving it superiority in management and scalability. Specifically, the scene categories in Million-AID are organized by the hierarchical category network of a three-level tree: 51 leaf nodes fall into 28 parent nodes at the second level which are grouped into 8 nodes at the first level, representing the 8 underlying scene categories of agriculture land, commercial land, industrial land, public service land, residential land, transportation land, unutilized land, and water area. The scene category network provides the dataset with excellent organization of relationship among different scene categories and also the property of scalability. The number of images in each scene category ranges from 2,000 to 45,000, endowing the dataset with the property of long tail distribution. Besides, Million-AID has superiorities over the existing scene classification datasets owing to its high spatial resolution, large scale, and global distribution.

Source: [DiRS: On Creating Benchmark Datasets for Remote Sensing Image Interpretation](https://arxiv.org/pdf/2006.12485)
Image Source: [https://captain-whu.github.io/DiRS/](https://captain-whu.github.io/DiRS/)

Introducing paper: On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
Edge Milling Heads
Full name: Edge Milling Heads

The **Edge Milling Heads** data set comprises 144 images of an edge profile cutting head of a milling machine. The head tool contains a total of 30 cutting inserts. The cutting head is formed by 6 diagonals of inserts in radial direction along the tool perimeter, encompassing 5 inserts per diagonal in axial direction. Positions of the last and first inserts of consecutive diagonals are aligned in the same vertical. Therefore, even though there are 30 inserts in total, there are 24 equally spaced positions of inserts along the tool perimeter. Additionally, inserts are squared shape with four 90º indexable cutting edges. Inserts are fastened with a screw. Rake angle is 0.

Images were taken with a monochrome camera Genie M1280 1/3’’ with active resolution of 1280 × 960 pixels. AZURE-2514MM fixed lens with 25 mm focal length were used.

Source: [Computer Vision Online](https://computervisiononline.com/dataset/1105138755)
MLe2e

MLe2 is a dataset for the evaluation of scene text end-to-end reading systems and all intermediate stages such as text detection, script identification and text recognition. The dataset contains a total of 711 scene images covering four different scripts (Latin, Chinese, Kannada, and Hangul).

Source: [A fine-grained approach to scene text script identification](https://arxiv.org/abs/1602.07475)

Introducing paper: A fine-grained approach to scene text script identification
VxC TSG
Full name: VxC TSG

The **VXC TSG** is based on samples taken from the ceramic tile industry and is comprised of 14 ceramic tile models, 42 surface grades and 960 pieces. It has been built in the VxC laboratory, at the Polytechnic University of Valencia, in collaboration with Keraben S.A., a large ceramic tile company located at Nules province of Castellón (Spain).

Source: [Computer Vision Online](https://computervisiononline.com/dataset/1105138707)
DADA-2000

DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. 

Source: [DADA: A Large-scale Benchmark and Model for Driver Attention Prediction in Accidental Scenarios](https://arxiv.org/pdf/1912.12148)
Image Source: [Fang et al](https://arxiv.org/pdf/1912.12148)

Introducing paper: DADA: Driver Attention Prediction in Driving Accident Scenarios
Panoramic Image Database
Full name: Panoramic Image Database

The **Panoramic Image Database** is a panoramic image dataset. The databases were collected by Andrew Vardy while visiting with the Computer Engineering group in February and March of 2004. Images were captured by a robot-mounted camera, pointed upwards at a hyperbolic mirror. The camera was an ImagingSource DFK 4303. The robot was an ActivMedia Pioneer 3-DX. The mirror was a large wide-view hyperbolic mirror from Accowle Ltd. The hyperbolic mirror expands the camera's field of view to allow the capture of panoramic images.
SESIV
Full name: SEmantic Salient Instance Video

SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.

Source: [Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation](https://arxiv.org/pdf/1807.01452)

Introducing paper: Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation
OTCBVS
Full name: OTCBVS

**OCTCBVS** is a benchmark dataset for testing and evaluating novel and state-of-the-art computer vision algorithms. The benchmark contains videos and images recorded in and beyond the visible spectrum and is available for free to all researchers in the international computer vision communities.

Source: [Computer Vision Online](https://computervisiononline.com/dataset/1105138704)
LEAF-QA

LEAF-QA, a comprehensive dataset of 250,000 densely annotated figures/charts, constructed from real-world open data sources, along with ~2 million question-answer (QA) pairs querying the structure and semantics of these charts. LEAF-QA highlights the problem of multimodal QA, which is notably different from conventional visual QA (VQA), and has recently gained interest in the community. Furthermore, LEAF-QA is significantly more complex than previous attempts at chart QA, viz. FigureQA and DVQA, which present only limited variations in chart data. LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering.

Source: [LEAF-QA: Locate, Encode & Attend for Figure Question Answering](https://arxiv.org/pdf/1907.12861)

Introducing paper: LEAF-QA: Locate, Encode & Attend for Figure Question Answering
Multi Task Crowd

Multi Task Crowd is a new 100 image dataset fully annotated for crowd counting, violent behaviour detection and density level classification.

Source: [ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification](https://arxiv.org/pdf/1705.10698)

Introducing paper: ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
DogCentric Activity
Full name: DogCentric Activity

The **DogCentric Activity** dataset is composed of dog activity videos taken from a first-person animal viewpoint. The dataset contains 10 different types of activities, including activities performed by the dog himself/herself, interactions between people and the dog, and activities performed by people or cars. 

The authors attached a GoPro camera to the back of each of the four dogs, and their owners took them on a walk to their familiar walking routes. The walking routes are in various environments, such as residential area, a park along a river, a sand beach, a field, streets with traffic, etc. Thus even though different dogs do the same activity, their background varies.

The video contains various activities with 10 activities of interest chosen as target activities: 'playing with a ball', 'waiting for a car to passed by', 'drinking water', 'feeding', 'turning dog's head to the left', 'turning dog's head to the right', 'petting', 'shaking dog's body by himself', 'sniffing', and 'walking'. The videos are in 320*240 image resolution, 48 frames per second.
Visual Question Answering v2.0
Full name: VQA v2.0

Visual Question Answering (VQA) v2.0 is a dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. It is the second version of the [VQA](https://www.paperswithcode.com/dataset/vqa) dataset.

- 265,016 images (COCO and abstract scenes)
- At least 3 questions (5.4 questions on average) per image
- 10 ground truth answers per question
- 3 plausible (but likely incorrect) answers per question
- Automatic evaluation metric

The [first version of the dataset](/dataset/visual-question-answering) was released in October 2015.

Introducing paper: Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Biwi Kinect Head Pose

Biwi Kinect Head Pose is a challenging dataset mainly inspired by the automotive setup.  It is acquired with the Microsoft Kinect sensor, a structured IR light device. It contains about 15k frame, with RGB. (640 × 480) and depth maps (640 × 480). Twenty subjects have been involved in the recordings: four of them were recorded twice, for a total of 24 sequences. The ground truth of yaw, pitch and roll angles is reported together with the head center and the calibration matrix. 

Source: [POSEidon: Face-from-Depth for Driver Pose Estimation](/paper/poseidon-face-from-depth-for-driver-pose)
ELAS

ELAS is a dataset for lane detection. It contains more than 20 different scenes (in more than 15,000 frames) and considers a variety of scenarios (urban road, highways, traffic, shadows, etc.). The dataset was manually annotated for several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).

Source: [Ego-Lane Analysis System (ELAS): Dataset and Algorithms](https://arxiv.org/abs/1806.05984)

Introducing paper: Ego-Lane Analysis System (ELAS): Dataset and Algorithms
100DOH
Full name: 100 Days Of Hands Dataset

The 100 Days Of Hands Dataset (100DOH) is a large-scale video dataset containing hands and hand-object interactions. It consists of 27.3K Youtube videos from 11 categories with nearly 131 days of footage of everyday interaction. The focus of the dataset is hand contact, and it includes both first-person and third-person perspectives. The videos in 100DOH are unconstrained and content-rich, ranging from records of daily life to specific instructional videos. To enforce diversity, the dataset contains no more than 20 videos from each uploader.

Source: [](http://fouheylab.eecs.umich.edu/~dandans/projects/100DOH/100DOH.html)

Introducing paper: Understanding Human Hands in Contact at Internet Scale
SVLD
Full name: Social Vision and Language Dataset

The social vision and language dataset is a large-scale multimodal dataset designed for research into social contextual learning.

Source: [A Dataset and Benchmarks for Multimedia Social Analysis](/paper/a-dataset-and-benchmarks-for-multimedia)
Image Source: [https://cannylab.github.io/svld/](https://cannylab.github.io/svld/)

Introducing paper: A Dataset and Benchmarks for Multimedia Social Analysis
TextComplexityDE

TextComplexityDE is a dataset consisting of 1000 sentences in German language taken from 23 Wikipedia articles in 3 different article-genres to be used for developing text-complexity predictor models and automatic text simplification in German language. The dataset includes subjective assessment of different text-complexity aspects provided by German learners in level A and B. In addition, it contains manual simplification of 250 of those sentences provided by native speakers and subjective assessment of the simplified sentences by participants from the target group. The subjective ratings were collected using both laboratory studies and crowdsourcing approach.

Source: [Subjective Assessment of Text Complexity: A Dataset for German Language](/paper/subjective-assessment-of-text-complexity-a)

Introducing paper: Subjective Assessment of Text Complexity: A Dataset for German Language
Famulus

This is a dataset for segmentation and classification of epistemic activities in diagnostic reasoning texts.

Source: [Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains](/paper/analysis-of-automatic-annotation-suggestions)

Introducing paper: Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains
CMU Wilderness Multilingual Speech Dataset

The CMU Wilderness Multilingual Speech Dataset is a dataset of over 700 different languages providing audio, aligned text and word pronunciations. On average each language provides around 20 hours of sentence-lengthed transcriptions. 

Source: [Alan W Black "CMU Wilderness Multilingual Speech Dataset" ICASSP 2019, Brighton, UK.](https://ieeexplore.ieee.org/document/8683536)
Aesthetic Visual Analysis
Full name: Aesthetic Visual Analysis

**Aesthetic Visual Analysis** is a dataset for aesthetic image assessment that contains over 250,000 images along with a rich variety of meta-data including a
large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style.
BigBIRD
Full name: Big Berkeley Instance Recognition Dataset

BigBIRD is a 3D dataset of 125 objects, with the following data for each object:

* 600 12 megapixel images, sampling the viewing hemisphere
* 600 registered RGB-D point clouds from a Carmine 1.09 sensor
* Pose information for each of the above images and point clouds
* Segmentation masks for each of the above images (and segmented point clouds)
* Merged point clouds consisting of data from all 600 viewpoints
* Reconstructed meshes from the merged point clouds

Paper: [ICRA 2014 "A Large-Scale 3D Database of Object Instances."](https://people.eecs.berkeley.edu/~pabbeel/papers/2014-ICRA-BigBIRD.pdf)

Introducing paper: BigBird: Big Data Storage and Analytics at Scale in Hybrid Cloud
CUHK Face Alignment Database

The CUHK Face Alignment Database is dataset with 13,466 face images, among which 5, 590 images are from LFW and the remaining 7, 876 images are downloaded from the web. Each face is labeled with the positions of five facial keypoints. 10,000 images are used for training and the remaining 3,466 images for validation.

<paper>

Image Source: [Deep Convolutional Network Cascade for Facial Point Detection](http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm)

Introducing paper: Deep Convolutional Network Cascade for Facial Point Detection
CUHK Square Dataset

CUHK Square data set is for transfer learning research on adapting generic pedestrian detectors. It includes a traffic video sequence of 60 minutes long. It is recorded by a stationary camera. The size of the scene is 720 by 576. 

In order to evaluate the performance of human detection on this data set, ground truth of pedestrians of some sampled frames are manually labeled. 

Paper Source: [Transferring a generic pedestrian detector towards specific scenes](https://doi.org/10.1109/CVPR.2012.6248064)

Source: [CUHK Square Dataset](http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_square/index.html)

Image Source: [CUHK Square Dataset](http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_square/index.html)
CUHK Occlusion Dataset

CUHK occlusion dataset includes 1,063 images with occluded pedestrians. It is used for Human Detection with occlusion handling in crowded scenes.

Paper: [A discriminative deep model for pedestrian detection with occlusion handling](https://doi.org/10.1109/CVPR.2012.6248062)

Source: [CUHK Occlusion Dataset](http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_occlusion/index.html)

Image Source: [CUHK Occlusion Dataset](http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_occlusion/index.html)
Grand Central Station Dataset

The Grand central station dataset includes a video with 50,010 frames which is used for Scene Understanding and Crowd Analysis.

Paper: [Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents](https://doi.org/10.1109/CVPR.2012.6248013)

Source: [Train Station Dataset](http://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html)

Image Source: [Train Station Dataset](http://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html)
ArtEmis

ArtEmis is a large-scale dataset aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, this dataset focuses on the affective experience triggered by visual artworks an the annotators were asked to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice. This leads to a rich set of signals for both the objective content and the affective impact of an image, creating associations with abstract concepts (e.g., “freedom” or “love”), or references that go beyond what is directly visible, including visual similes and metaphors, or subjective references to personal experiences. 

This dataset focuses on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. ArtEmis contains 439K emotion attributions and explanations from humans, on 81K artworks from WikiArt.

Paper: [ArtEmis: Affective Language for Visual Art](https://arxiv.org/abs/2101.07396)

Source: [ArtEmis Dataest](https://www.artemisdataset.org/)

Image Source: [ArtEmis Dataest](https://www.artemisdataset.org/)

Introducing paper: ArtEmis: Affective Language for Visual Art
BreakHis
Full name: Breast Cancer Histopathological Database

The Breast Cancer Histopathological Image Classification (BreakHis) is  composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X).  It contains 2,480  benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.

Paper: [F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264](https://doi.org/10.1109/TBME.2015.2496264)

Source: [https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)

Image Source: [https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/)
2D Hela

2D HeLa is a dataset of fluorescence microscopy images of HeLa cells stained with various organelle-specific fluorescent dyes. The images include 10 organelles, which are DNA (Nuclei), ER (Endoplasmic reticulum), Giantin, (cis/medial Golgi), GPP130 (cis Golgi), Lamp2 (Lysosomes), Mitochondria, Nucleolin (Nucleoli), Actin, TfR (Endosomes), Tubulin.
The purpose of the dataset is to train a computer program to automatically identify sub-cellular organelles.


Paper: [M. V. Boland and R. F. Murphy (2001). A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioinformatics 17:1213-1223](https://doi.org/10.1093/bioinformatics/17.12.1213)

Source: [Identifying Sub-cellular Organelles](https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html)

Image Source: [Identifying Sub-cellular Organelles](https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html)
PointPattern

PointPattern is a graph classification dataset constructed by simple point patterns from statistical mechanics. The authors simulated three point patterns in 2D: hard disks in equilibrium (HD), Poisson point process, and random sequential adsorption (RSA) of disks. The HD and Poisson distributions can be seen as simple models that describe the microstructures of liquids and gases while the RSA is a nonequilibrium stochastic process that introduces new particles one by one subject to nonoverlapping conditions. 

These systems are well known to be structurally different, while being easy to simulate, thus provides a solid and controllable classification task. For each point pattern, the
particles are treated as nodes, and edges are subsequently drawn according to whether two particles are within a threshold distance.

Source: [Path Integral Based Convolution and Pooling for Graph Neural Networks](https://arxiv.org/pdf/2006.16811.pdf)
Image Source: [Path Integral Based Convolution and Pooling for Graph Neural Networks](https://arxiv.org/pdf/2006.16811.pdf)

Introducing paper: Path Integral Based Convolution and Pooling for Graph Neural Networks
Humans in 3D

H3D (Humans in 3D) is a dataset of annotated people. The annotations include:

* The joints and other keypoints (eyes, ears, nose, shoulders, elbows, wrists, hips, knees and ankles)
* The 3D pose inferred from the keypoints.
* Visibility boolean for each keypoint
* Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder)
* Body type (male, female or child)

Paper: [Poselets: Body part detectors trained using 3D human pose annotations](https://doi.org/10.1109/ICCV.2009.5459303)

Source: [H3D](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/)

Image Source: [H3D](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/)
BelgaLogos

BelgaLogos is a dataset for logo detection and recognition. The images of BelgaLogos dataset have been provided and are copyrighted by BELGA press agency. They are freely available for research purpose only. The dataset is composed of 10,000 images covering all aspects of life and current affairs: politics and economics, finance and social affairs, sports, culture and personalities. All images are in JPEG format and have been re-sized with a maximum value of height and width equal to 800 pixels, preserving aspect ratio. 

Paper: [Alexis Joly and Olivier Buisson, Logo retrieval with a contrario visual query expansion, In Proceedings of the Seventeen ACM international Conference on Multimedia, 2009.](https://doi.org/10.1145/1631272.1631361)

Source: [BelgaLogos dataset](http://www-sop.inria.fr/members/Alexis.Joly/BelgaLogos/BelgaLogos.html)

Image Source: [BelgaLogos dataset](http://www-sop.inria.fr/members/Alexis.Joly/BelgaLogos/BelgaLogos.html)
Aspects dataset

This dataset contains video shots for two different classes: tigers and cars. The shots were collected from 188 car ads (~1–2 min each) and 14 nature documentaries about tigers (~40 min), amounting to roughly 14 h of video. The videos were partitioned into shorter shots, and only those showing at least one instance of the class were kept. This produced 806 shots for the car and 1880 for the tiger class, typically 1–100 sec in length.

Paper: [Discovering object aspects from video](https://doi.org/10.1016/j.imavis.2016.04.014)

Source: [Aspects dataset](http://calvin-vision.net/aspects-dataset/)

Image Source: [Aspects dataset](http://calvin-vision.net/aspects-dataset/)
POET
Full name: Pascal Objects Eye Tracking

The POET (Pascal Objects Eye Tracking) is a dataset that consists of eye tracking data for the complete trainval set of ten objects classes (cat, dog, bicycle, motorbike, boat, aeroplane, horse, cow, sofa, dining table) from [Pascal VOC 2012](pascal-voc) (6,270 images in total). Each image is annotated with the eye movement record of five participants, whose task was to identify which object class was present in the image.

Paper: [Training object class detectors from eye tracking data](https://doi.org/10.1007/978-3-319-10602-1_24)

Source: [Pascal Objects Eye Tracking (POET) v1.1](http://calvin-vision.net/datasets/poet-dataset/)

Image Source: [Pascal Objects Eye Tracking (POET) v1.1](http://calvin-vision.net/datasets/poet-dataset/)
AMUSE
Full name: Automotive Multi-Sensor Dataset

The automotive multi-sensor (AMUSE) dataset consists of inertial and other complementary sensor data combined with monocular, omnidirectional, high frame rate visual data taken in real traffic scenes during multiple test drives.

Paper: [A Multi-sensor Traffic Scene Dataset with Omnidirectional Video](https://doi.org/10.1109/CVPRW.2013.110)

Source: [AMUSE](http://www.cvl.isy.liu.se/en/research/datasets/amuse/)

Image Source: [AMUSE](http://www.cvl.isy.liu.se/en/research/datasets/amuse/)
IMO
Full name: Independently Moving Objects

Dataset of annotated independently moving objects (IMO). This dataset contains left and right images, stereo images, stereo disparity from SGM, and vehicle labels as well as a ground truth annotations. 

Paper: [Independently Moving Object Trajectories from Sequential Hierarchical Ransac](https://users.isy.liu.se/cvl/perfo/abstracts/persson21.html)

Source: [IMO Dataset](http://www.cvl.isy.liu.se/en/research/datasets/imo-dataset/)

Image Source: [IMO Dataset](http://www.cvl.isy.liu.se/en/research/datasets/imo-dataset/)
LTIR
Full name: Linköping Thermal InfraRed

The LTIR dataset is a thermal infrared dataset for evaluation of Short-Term Single-Object (STSO) tracking.

The dataset contains

* 20 thermal infrared sequences, one .png per frame. Some sequences are available in both 8- and 16-bits.
* Bounding box annotations of one object per sequence.
* Local per-frame annotations.

Paper: [A thermal Object Tracking benchmark](https://doi.org/10.1109/AVSS.2015.7301772)

Source: [The LTIR dataset v1.0](http://www.cvl.isy.liu.se/en/research/datasets/ltir/version1.0/)

Image Source: [The LTIR dataset v1.0](http://www.cvl.isy.liu.se/en/research/datasets/ltir/version1.0/)
Family101

The Family101 dataset is the a large-scale dataset of families across several generations. It contains 101 different families with distinct family names, including 206 nuclear families, 607 individuals, with 14,816 images. The dataset are composed of renowned public families.

Paper: [Kinship Classification by Modeling Facial Feature Heredity People](https://doi.org/10.1109/ICIP.2013.6738614)

Source: [Kinship Classification by Modeling Facial Feature Heredity People](http://chenlab.ece.cornell.edu/projects/KinshipClassification/index.html)

Image Source: [Kinship Classification by Modeling Facial Feature Heredity People](http://chenlab.ece.cornell.edu/projects/KinshipClassification/index.html)
KinFaceW

KinFaceW consists of two kinship datasets: KinFaceW-I and KinFaceW-II. Face images were collected from the internet, including some public figure face images as well as their parents' or children's face images. Face images are captured under uncontrolled environments in two datasets with no restriction in terms of pose, lighting, background, expression, age, ethnicity, and partial occlusion. The difference of KinFaceW-I and KinFaceW-II is that face images with a kin relation were acquired from different photos in KinFaceW-I and the same photo in KinFaceW-II in most cases.

Paper: [Neighborhood Repulsed Metric Learning for Kinship Verification](https://doi.org/10.1109/CVPR.2012.6247978)

Source: [KinFaceW Database](https://www.kinfacew.com/datasets.html)

Image Source: [KinFaceW Database](https://www.kinfacew.com/datasets.html)
Boxy
Full name: Boxy Vehicles Dataset

A large vehicle detection dataset with almost two million annotated vehicles for training and evaluating object detection methods for self-driving cars on freeways.

The dataset consists of:

* 200,000 images
* 1,990,000 annotated vehicles
* 5 Megapixel resolution
* Sunshine, rain, dusk, night
* Clear freeways, heavy traffic, traffic jams

Paper: [Boxy Vehicle Detection in Large Images](https://doi.org/10.1109/ICCVW.2019.00112)

Source: [Boxy](https://boxy-dataset.com/boxy/)

Image Source: [Boxy](https://boxy-dataset.com/boxy/)
CASR
Full name: Cyclist Arm Signal Recognition

CASR is a dataset for cyclist arm signal recognition in videos. It contains 219 annotated arm signal actions on videos of approximately 10 seconds each, containing one or two actions per video.

Source: [Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation](https://arxiv.org/pdf/1910.03858.pdf)

Image Source: [Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation](https://arxiv.org/pdf/1910.03858.pdf)

Introducing paper: Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
Driving Event Camera Dataset

This dataset consists of a number of sequences that were recorded with a VGA (640x480) event camera (Samsung DVS Gen3) and a conventional RGB camera (Huawei P20 Pro) placed on the windshield of a car driving through Zurich.

Source: [Driving Event Camera Dataset (Samsung DVS Gen3)](http://rpg.ifi.uzh.ch/event_driving_datasets.html)

Image Source: [Driving Event Camera Dataset (Samsung DVS Gen3)](http://rpg.ifi.uzh.ch/event_driving_datasets.html)

Introducing paper: High Speed and High Dynamic Range Video with an Event Camera
FRIDA
Full name: Foggy Road Image Database

FRIDA and FRIDA2 are databases of numerical images easily usable to evaluate in a systematic way the performance of visibility and contrast restoration algorithms. FRIDA comprises 90 synthetic images of 18 urban road scenes. FRIDA2 comprises 330 synthetic images of 66 diverse road scenes. The view point is closed to the one of the vehicle's driver. To each image without fog is associated 4 foggy images and a depthmap. Different kind of fog are added on each of the 4 associated images: uniform fog, heterogeneous fog, cloudy fog, and cloudy heterogeneous fog. These scenes can be used to test visibility and contrast restoration algorithms intensively and in an objective way, as well as "shape from fog" algorithms. The calibration parameters of the camera are given. 

Paper: [Improved Visibility of Road Scene Images under Heterogeneous Fog](https://doi.org/10.1109/IVS.2010.5548128)

Source: [FRIDA (Foggy Road Image DAtabase) image database](http://perso.lcpc.fr/tarel.jean-philippe/bdd/frida.html)

Image Source: [FRIDA (Foggy Road Image DAtabase) image database](http://perso.lcpc.fr/tarel.jean-philippe/bdd/frida.html)
Ford Campus Vision and Lidar Data Set

Ford Campus Vision and Lidar Data Set is a dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. 

This dataset consists of the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research campus and downtown Dearborn, Michigan during November-December 2009. The vehicle path trajectory in these datasets contain several large and small-scale loop closures, which should be useful for testing various state of the art computer vision and SLAM (Simultaneous Localization and Mapping) algorithms.

Paper: [Ford Campus vision and lidar data set](https://doi.org/10.1177%2F0278364911400640)

Source: [Ford Campus vision and lidar data set](http://robots.engin.umich.edu/SoftwareData/Ford)

Image Source: [Ford Campus vision and lidar data set](https://doi.org/10.1177%2F0278364911400640)