dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
DR-VCTK | This dataset is a new variant of the voice cloning toolkit (VCTK) dataset: device-recorded VCTK (DR-VCTK), where the high-quality speech signals recorded in a semi-anechoic chamber using professional audio devices are played back and re-recorded in office environments using relatively inexpensive consumer devices. | Provide a detailed description of the following dataset: DR-VCTK |
IoT Inspector | **IoT Inspector** is a large dataset of labeled network traffic from smart home devices from within real-world home networks. It is used to conduct data-driven smart home research. An open source tool with the same name has been used to collect data from 44,956 smart home devices across 13 categories and 53 vendors. | Provide a detailed description of the following dataset: IoT Inspector |
BugSwarm | **BugSwarm** is a dataset of reproducible faults and fixes to perform experimental evaluation of approaches to software quality. The BugSwarm toolkit has already gathered 3,091 fail-pass pairs, in Java and Python, all packaged within fully reproducible containers. | Provide a detailed description of the following dataset: BugSwarm |
Peer to Peer Hate | **Peer to Peer Hate** is a comprehensive hate speech dataset capturing various types of hate. It has been built from 27,330 hate speech tweets. | Provide a detailed description of the following dataset: Peer to Peer Hate |
Dense Forest Trail | **Dense Forest Trail** is an UAV dataset collected from a variety of simulated environment in Unreal Engine. | Provide a detailed description of the following dataset: Dense Forest Trail |
MengeROS | MengeROS is an open-source crowd simulation tool for robot navigation that integrates Menge with ROS. It extends Menge to introduce one or more robot agents into a crowd of pedestrians. Each robot agent is controlled by external ROS-compatible controllers. MengeROS has been used to simulate crowds with up to 1000 pedestrians and 20 robots. | Provide a detailed description of the following dataset: MengeROS |
Dizi | **Dizi** is a dataset of music style of the Northern school and the Southern School. Characteristics include melody and playing techniques of the two different music styles are deconstructed. | Provide a detailed description of the following dataset: Dizi |
P3 | A set of patterns used in psychophysical research to evaluate the ability of saliency algorithms to find targets distinct from distractors in orientation, color and size. Each image is a 7x7 grid and contains a single target. All images are 1024x1024px and have corresponding ground truth masks for the target and distractors. | Provide a detailed description of the following dataset: P3 |
O3 | A set of realistic odd-one-out stimuli gathered "in the wild". Each image in the Odd-One-Out (O3) dataset depicts a scene with multiple objects similar to each other in appearance (distractors) and a singleton (target) distinct in one or more feature dimensions (e.g. color, shape, size). All images are resized so that the larger dimension is 1024px. Targets represent approx. 400 common object types such as flowers, sweets, chicken eggs, leaves, tiles and birds. Pixelwise masks are provided for targets and distractors. Annotations are generated using CVAT. | Provide a detailed description of the following dataset: O3 |
KvasirCapsule-SEG | The dataset contains a Video capsule endoscopy dataset for polyp segmentation.
The dataset can be downloaded from here:
https://www.kaggle.com/debeshjha1/kvasircapsuleseg
https://www.dropbox.com/home/KvasirCapsule-SEG
The detail about the dataset can be found from
https://arxiv.org/pdf/2104.11138.pdf | Provide a detailed description of the following dataset: KvasirCapsule-SEG |
Kvasir-Sessile dataset | The Kvasir-SEG dataset includes 196 polyps smaller than 10 mm classified as Paris class 1 sessile or Paris class IIa. We have selected it with the help of expert gastroenterologists. We have released this dataset separately as a subset of Kvasir-SEG. We call this subset Kvasir-Sessile.
The dataset is publicly available. It can be downloaded from here:
https://drive.google.com/drive/folders/1OjsStQh6yuKz0bG6OA3BzmIiXDZILg7V?usp=sharing
If you use this dataset, please cite our paper,
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9314114
/https://arxiv.org/pdf/1911.07069.pdf
if you use our dataset, | Provide a detailed description of the following dataset: Kvasir-Sessile dataset |
Kvasir-Capsule | Kvasir-Capsule dataset is the largest publicly released VCE dataset. In total, the dataset contains 47,238 labeled images and 117 videos, where it captures anatomical landmarks and pathological and normal findings. The results is more than 4,741,621 images and video frames altogether. | Provide a detailed description of the following dataset: Kvasir-Capsule |
Hyper-Kvasir Dataset | HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether. | Provide a detailed description of the following dataset: Hyper-Kvasir Dataset |
pathbased | **pathbased** is a 3-cluster data set. The data set consists of a circular cluster with an opening near the bottom and two Gaussian distributed clusters inside. Each cluster contains 100 data points. | Provide a detailed description of the following dataset: pathbased |
Gun Detection Dataset | This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources. | Provide a detailed description of the following dataset: Gun Detection Dataset |
RADDet | **RADDet** is a radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. It is used to train and evaluate methods for object detection using automotive radars. | Provide a detailed description of the following dataset: RADDet |
RoadAnomaly21 | **RoadAnomaly21** is a dataset for anomaly segmentation, the task of identify the image regions containing objects that have never been seen during training. It consists of an evaluation dataset of 100 images with pixel-level annotations. Each image contains at least one anomalous object, e.g. animals or unknown vehicles. The anomalies can appear anywhere in the image and widely differ in size, covering from 0.5% to 40% of the image | Provide a detailed description of the following dataset: RoadAnomaly21 |
MARS Map | **MARS Map** is a set of three dataset collected to evaluate the performance of mapping algorithms within a room and between rooms. | Provide a detailed description of the following dataset: MARS Map |
FFT-75 | The **FFT-75** dataset contains randomly sampled, potentially overlapping file fragments from 75 popular file types. It is a diverse and balanced dataset which is labeled with class IDs and is ready for training supervised machine learning models. We distinguish 6 different scenarios with different granularity and provide variants with 512 and 4096-byte blocks. In each case, we sampled a balanced dataset and split the data as follows: 80% for training, 10% for testing and 10% for validation. | Provide a detailed description of the following dataset: FFT-75 |
Windows PE Malware | This is a dataset for the task of PE-type malware in the Windows operating system. The different samples in the dataset are classified into 8 main malware families: Trojan, Backdoor, Downloader, Worms, Spyware Adware, Dropper, Virus. | Provide a detailed description of the following dataset: Windows PE Malware |
Bonn RGB-D Dynamic | **Bonn RGB-D Dynamic** is a dataset for RGB-D SLAM, containing highly dynamic sequences. We provide 24 dynamic sequences, where people perform different tasks, such as manipulating boxes or playing with balloons, plus 2 static sequences. For each scene we provide the ground truth pose of the sensor, recorded with an Optitrack Prime 13 motion capture system. The sequences are in the same format as the TUM RGB-D Dataset, so that the same evaluation tools can be used. Furthermore, we provide a ground truth 3D point cloud of the static environment recorded using a Leica BLK360 terrestrial laser scanner. | Provide a detailed description of the following dataset: Bonn RGB-D Dynamic |
VMRD | **VMRD** is a multi-object grasp dataset. It has been collected and labeled using hundreds of objects coming from 31 categories. There are totally 5,185 images including 17,688 object instances and 51,530 manipulation relationships. | Provide a detailed description of the following dataset: VMRD |
TSP/HCP Benchmark set | This is a benchmark set for Traveling salesman problem (TSP) with characteristics that are different from the existing benchmark sets. In particular, it focuses on small instances which prove to be challenging for one or more state-of-the-art TSP algorithms. These instances are based on difficult instances of Hamiltonian cycle problem (HCP). This includes instances from literature, specially modified randomly generated instances, and instances arising from the conversion of other difficult problems to HCP. | Provide a detailed description of the following dataset: TSP/HCP Benchmark set |
Logic Bombs | This is a set of small programs with logic bombs. The logic bomb can be triggered when certain conditions are met. Any dynamic testing tools (especially symbolic execution) can employ the dataset to benchmark their capabilities. | Provide a detailed description of the following dataset: Logic Bombs |
CapriDB | **CapriDB** is a 3D object database for robotics. | Provide a detailed description of the following dataset: CapriDB |
Xamarin Q&A | **Xamarin Q&A** consists of two datasets of questions and answers for studying the development of cross-platform mobile applications using the Xamarin framework. The two datasets were created by mining two Q&A sites: Xamarin Forum and Stack Overflow. The datasets have 85,908 questions mined from the Xamarin Forum and 44,434 from Stack Overflow. | Provide a detailed description of the following dataset: Xamarin Q&A |
3DSSG | 3DSSG provides 3D semantic scene graphs for 3RScan. A semantic scene graph is defined by a set of tuples between nodes and edges where nodes represent specific 3D object instances in a 3D scan. Nodes are defined by its semantics, a hierarchy of classes as well as a set of attributes that describe the visual and physical appearance of the object instance and their affordances. The edges in our graphs are the semantic relationships (predicates) between the nodes such as `standing on, hanging on, more comfortable than` or `same material`. | Provide a detailed description of the following dataset: 3DSSG |
AudioCaps | **AudioCaps** is a dataset of sounds with event descriptions that was introduced for the task of audio captioning, with sounds sourced from the [AudioSet](https://paperswithcode.com/dataset/audioset) dataset. Annotators were provided the audio tracks together with category hints (and with additional video hints if needed). | Provide a detailed description of the following dataset: AudioCaps |
ToolNet | The dataset is organized as follows. We have 8 different goals and 10 different world instances for both the domains, home and factory. Each domain has 8 directories corresponding to the goals possible for the domain. These goals itself, contain directories for the 10 different world instances. Each goal for each world instance in a particular domain thus has a number of different human demonstrations, and these are saved in the form of a .datapoint file for each plan. | Provide a detailed description of the following dataset: ToolNet |
CollATe | The **CollATe** dataset is large dataset consisting of two types of collusive entities on YouTube – videos submitted to gain collusive likes and comment requests, and channels submitted to gain collusive subscriptions. | Provide a detailed description of the following dataset: CollATe |
Semantic Trails | Semantic Trails Datasets (STDs) are two different datasets of semantically annotated trails created starting from check-ins performed on the Foursquare social network. | Provide a detailed description of the following dataset: Semantic Trails |
360 EM | Data set of 360-degree equirectangular videos, gaze recordings, eye movement (EM) ground-truth and an automatic EM classification algorithm. | Provide a detailed description of the following dataset: 360 EM |
Innovation and Revenue | This is a dataset that catalogs 2.6 million patents granted between 2005 and 2017. | Provide a detailed description of the following dataset: Innovation and Revenue |
Pull Request Descriptions | This is a dataset of over 333K Pull Requests, used for automatic pull request description generation. | Provide a detailed description of the following dataset: Pull Request Descriptions |
Reddit Norm Violations | This is a dataset of over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated. | Provide a detailed description of the following dataset: Reddit Norm Violations |
DBLP Temporal | **DBLP Temporal** is a dataset for temporal entity resolution, based on author profiles extracted from the Digital Bibliography and Library Project (DBLP). | Provide a detailed description of the following dataset: DBLP Temporal |
Rediscovery Datasets | We present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. | Provide a detailed description of the following dataset: Rediscovery Datasets |
FLOBOT Perception | This dataset was collected with FLOBOT - an advanced autonomous floor scrubber - includes data from four different sensors for environment perception, as well as the robot pose in the world reference frame. Specifically, FLOBOT relies on a 3D lidar and a RGB-D camera for human detection and tracking, and a second RGB-D and a stereo camera for dirt and object detection. Data collection was performed in four public places (three of them are released in this dataset), two in Italy and two in France, in FOLBOT working mode with the corresponding testing procedures for final project validation | Provide a detailed description of the following dataset: FLOBOT Perception |
Collision Avoidance Challenge dataset | The Collision Avoidance Challenge dataset is the official dataset used during the **ESA's Kelvins competition for "Collision Avoidance Challenge"**. The dataset is a collection of Conjunction Data Messages (CDMs) received by ESA from 2015 to 2019. The CDMs have been anonymised for distribution. The initial raw data, as well as the labels that were kept private during the competition, are also released.
ESA thanks the US Space Surveillance Network for the provision of surveillance data supporting safe operations of ESA’s spacecraft. In addition, we are grateful to the agreement which allows to publicly release the current dataset.
The dataset is represented as a table, where each row corresponds to a single CDM, and each CDM contains 103 recorded characteristics/features. There are thus 103 columns, which are described in the competition pages. The dataset is made of several unique collision/close approach events, which are identified in the `event_id` column. In turn, each collision event is made of several CDMs recorded over time. Therefore, a single collision event can be thought of as a times series of CDMs. From these CDMs, for every collision event, we are interested in predicting the final risk which is computed in the last CDM of the time series (i.e. the risk value in the last row of each collision event).
For a detailed description on the challenge and this dataset, visit [https://kelvins.esa.int/collision-avoidance-challenge/data/](https://kelvins.esa.int/collision-avoidance-challenge/data/).
The paper describing the competition setup and result can be found at [https://arxiv.org/pdf/2008.03069.pdf](https://arxiv.org/pdf/2008.03069.pdf). | Provide a detailed description of the following dataset: Collision Avoidance Challenge dataset |
OREBA | The OREBA dataset aims to provide a comprehensive multi-sensor recording of communal intake occasions for researchers interested in automatic detection of intake gestures. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands. | Provide a detailed description of the following dataset: OREBA |
IWSLT2015 | The IWSLT 2015 Evaluation Campaign featured three tracks: automatic speech recognition (ASR), spoken language translation (SLT), and machine translation (MT). For ASR we offered two tasks, on English and German, while
for SLT and MT a number of tasks were proposed, involving English, German, French, Chinese, Czech, Thai, and Vietnamese. All tracks involved the transcription or translation of TED talks, either made available by the official TED website or by other TEDx events. A notable change with respect to previous evaluations was the use of unsegmented speech in the SLT track in order to better fit a real application scenario. | Provide a detailed description of the following dataset: IWSLT2015 |
MIMII DUE | This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, pumps, slide rails, and valves. The data for each machine type includes six subsets called "sections'', and each section roughly corresponds to a single product. Each section consists of data from two domains, called the source domain and the target domain, with different conditions such as operating speed and environmental noise. This dataset is a subset of the dataset for DCASE 2021 Challenge Task 2, so the dataset is entirely the same as data included in the development dataset and additional training dataset. | Provide a detailed description of the following dataset: MIMII DUE |
DroneCrowd | **DroneCrowd** is a benchmark for object detection, tracking and counting algorithms in drone-captured videos. It is a drone-captured large scale dataset formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, it has annotations for 20,800 people trajectories with 4.8 million heads and several video-level attributes. | Provide a detailed description of the following dataset: DroneCrowd |
Content4All | **Content4All** is a collection of six open research datasets aimed at automatic sign language translation research.
Sign language interpretation footage was captured by the broadcast partners SWISSTXT and VRT. Raw footage was anonymized and processed to extract 2D and 3D human
body pose information. From the roughly 190 hours of processed data, three base (RAW) datasets were released, namely 1) **SWISSTXT-RAW-NEWS**, 2) **SWISSTXT-RAW-WEATHER** and 3) **VRT-RAW**. Each dataset contains sign language interpretations, corresponding spoken language subtitles, and extracted 2D/3D human pose information.
A subset from each base dataset was selected and manually annotated to align spoken language subtitles and sign language interpretations. The subset selection was
done to resemble the benchmark Phoenix 2014T dataset. Our aim is for these three new annotated public datasets, namely 4) **SWISSTXT-NEWS**, 5) **SWISSTXT-WEATHER**
and 6) **VRT-NEWS** to become benchmarks and underpin future research as the field moves closer to translation and production on larger domains of discourse. | Provide a detailed description of the following dataset: Content4All |
MIAP | **MIAP** is a dataset created by obtaining a new set of annotations on a subset of the [Open Images](/dataset/open-images-v4) dataset, containing bounding boxes and attributes for all of the people visible in those images, as the original Open Images dataset annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image.
The MIAP dataset focuses on enabling ML Fairness research. It provides additional annotations for 100,000 (70k from training and 30k from validation/test) images that contain at least one person bounding box in the original annotations.
These additional annotations provide exhaustive bounding boxes for all people in an image. Person boxes are further annotated with attribute labels for fairness research. Annotated attributes include the human perceived gender presentation (predominantly feminine, predominantly masculine, and unknown) and perceived age range (young, middle, older, and unknown) of the localized person. This procedure adds nearly 100,000 new boxes that were not annotated under the original labeling pipeline.
Annotations on the exhaustive set enable research into the fairness properties of models trained on partial annotations and the pipelines that produce these annotations. | Provide a detailed description of the following dataset: MIAP |
MQTT-IoT-IDS2020 | Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models.
**MQTT-IoT-IDS2020** is the first dataset to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture. The network comprises twelve sensors, a broker, a simulated camera, and an attacker. Five scenarios are recorded: (1) normal operation, (2) aggressive scan, (3) UDP scan, (4) Sparta SSH brute-force, and (5) MQTT brute-force attack. The raw pcap files are saved, then features are extracted. Three abstraction levels of features are extracted from the raw pcap files: (a) packet features, (b) Unidirectional flow features and (c) Bidirectional flow features. The csv feature files in the dataset are suited for Machine Learning (ML) usage. Also, the raw pcap files are suitable for the deeper analysis of MQTT IoT networks communication and the associated attacks. | Provide a detailed description of the following dataset: MQTT-IoT-IDS2020 |
Backstabber’s Knife Collection | Backstabber’s Knife Collection is a dataset of 174 malicious software packages that were used in real-world attacks on open source software supply chains, and which were distributed via the popular package repositories npm, PyPI, and RubyGems. Those packages, dating from November 2015 to November 2019, were manually collected and analyzed. | Provide a detailed description of the following dataset: Backstabber’s Knife Collection |
Ukiyo-e Faces | The ukiyo-e faces dataset comprises of 5209 images of faces from ukiyo-e prints. The images are 1024x1024 pixels in jpeg format and have been aligned using the procedure used for the FFHQ dataset | Provide a detailed description of the following dataset: Ukiyo-e Faces |
Data Loss repository | This is a benchmark of data loss bugs for android apps. It is a public benchmark of 110 data loss faults in Android apps that we systematically collected to facilitate research and experimentation with these problems. The benchmark is available on GitLab and includes the faulty apps, the fixed apps (when available), the test cases to automatically reproduce the problems, and additional information that may help researchers in their tasks. | Provide a detailed description of the following dataset: Data Loss repository |
RePack | **RePack** is a dataset to study the detection of repackaged Android apps. | Provide a detailed description of the following dataset: RePack |
PFN-VT | **PFN-VT** is a dataset for the estimation of tactile properties from vision, such as slipperiness or roughness. The dataset is collected with a webcam and uSkin tactile sensor mounted on the end-effector of a Sawyer robot, which strokes the surfaces of 25 different materials. | Provide a detailed description of the following dataset: PFN-VT |
Twitter Abusive Behavior | 80k tweets annotated concerning Inappropriate Speech (more particularly in matters of Abusive and Hateful speech) as well as Normal and Spam. | Provide a detailed description of the following dataset: Twitter Abusive Behavior |
CinemAirSim | **CinemAirSim** is an extension of the well-known drone simulator, [AirSim](airsim), with a cinematic camera as well as extended its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. | Provide a detailed description of the following dataset: CinemAirSim |
ICDCN2019 | This is a dataset consisting of complete network traces comprising benign and malicious traffic, which is feature-rich and publicly available. | Provide a detailed description of the following dataset: ICDCN2019 |
Mal-Activity | This is a dataset of Internet malicious activity (mal-activity in short). It contains more than 51 million mal-activity reports involving 662K unique IP addresses covering the period form January 2007 to June 2017. Leveraging the Wayback Machine, antivirus (AV) tool reports and several additional public datasets (e.g., BGP Route Views and Internet registries) the data is enriched with historical meta-information including geo-locations (countries), autonomous system (AS) numbers and types of mal-activity.
An initially labelled dataset of approx 1.57 million mal-activities (obtained from public blacklists) is used to train a machine learning classifier to classify the remaining unlabeled dataset of approx 44 million mal-activities obtained through additional sources. | Provide a detailed description of the following dataset: Mal-Activity |
α-Satellite | This is a collection of datasets related to Covid-19. It consists of large scale and real-time pandemic related data from multiple sources, including disease related data from official public health organizations and digital media, demographic data, mobility data, and user generated data from social media (i.e., Reddit). | Provide a detailed description of the following dataset: α-Satellite |
VAST Absorption | **VAST Absorption** is a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls’ absorption coefficients. | Provide a detailed description of the following dataset: VAST Absorption |
On the Origins of Memes by Means of Fringe Web Communities | This dataset was collected with research funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 691025.
The dataset consists of all the URLs and phashes for images from Twitter, Reddit, 4chan's /pol/, and Gab posted between July 2016 and end of July 2017. | Provide a detailed description of the following dataset: On the Origins of Memes by Means of Fringe Web Communities |
Bimanual Actions Dataset | The Bimanual Actions Dataset is a collection of 540 RGB-D videos, showing subjects perform bimanual actions in a kitchen or workshop context. The main purpose for its compilation is to research bimanual human behaviour in order to eventually improve the capabilities of humanoid robots. | Provide a detailed description of the following dataset: Bimanual Actions Dataset |
ColosseumRL | **ColosseumRL** is a framework for research in reinforcement learning in n-player games.
ColosseumRL contains a number of multiagent free-for-all games. Currently, we have Tron, Blokus, and 3 and 4-player tic-tac-toe. In the future, we will be adding Chinese checkers and other similar games. Tron is a fully-observable multiagent free-for-all turn-based snake variant where players try to survive the longest without crashing into walls or each other. Blokus is a fully-observable multiagent free-for-all turn-based game in which players place pieces on a board to claim space and strategically block opponents from placing their own pieces. | Provide a detailed description of the following dataset: ColosseumRL |
Online Cryptocurrency-topic diffusion on Twitter, Telegram, and Discord | This Dataset is described in Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020), a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.
Please, refer to the paper below for more details.
Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).
This dataset is composed of:
~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;
~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);
~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight). | Provide a detailed description of the following dataset: Online Cryptocurrency-topic diffusion on Twitter, Telegram, and Discord |
3D-Printing-Data | This is a dataset for anomalies detection in 3D printing. | Provide a detailed description of the following dataset: 3D-Printing-Data |
Grasp Affordance | This is a dataset for visual grasp affordance prediction that promotes more robust and heterogeneous robotic grasping methods. The dataset contains different attributes from 30 different objects. Each object instance is related not only to the semantic descriptions, but also the physical features describing visual attributes, locations and different grasping regions related to a variety of actions. | Provide a detailed description of the following dataset: Grasp Affordance |
DeformingThings4D | **DeformingThings4D** is a synthetic dataset containing 1,972 animation sequences spanning 31 categories of humanoids and animals. It provides 200 animations for humanoids and 1772 animations for animals.
#### Use case of the dataset
The dataset is designed to tackle the following tasks using data-driven approaches:
- Scene flow estimation
- Non-rigid tracking/registration
- Shape and motion completion
- Learning riggings from observation
- Generic non-rigid reconstruction | Provide a detailed description of the following dataset: DeformingThings4D |
Visual Servoing | Dataset for visual servoing (VS) and camera pose estimation.
The images were obtained by a manipulator robot with an eye-in-hand camera in different poses.
The labels represent the camera pose.
It is possible to obtain the absolute pose of the camera without any pre-processing of the dataset, as well as the relative pose between images through matrix transformations.
One may also use the dataset to get the camera's 6DoF speeds so that the visual servo control between two images can be performed. | Provide a detailed description of the following dataset: Visual Servoing |
DeepStab | **DeepStab** is a dataset for online video stabilization consisting of synchronized steady/unsteady video pairs collected via a well designed hand-held hardware. | Provide a detailed description of the following dataset: DeepStab |
CxC | Crisscrossed Captions (CxC) contains 247,315 human-labeled annotations including positive and negative associations between image pairs, caption pairs and image-caption pairs.
Image source: [Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO](https://arxiv.org/pdf/2004.15020.pdf) | Provide a detailed description of the following dataset: CxC |
AdversarialQA | We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. | Provide a detailed description of the following dataset: AdversarialQA |
3DCSR dataset | Cross-source point cloud dataset for registration task. It includes point clouds from structure from motion (SFM), Kinect, Lidar. | Provide a detailed description of the following dataset: 3DCSR dataset |
DTU | DTU MVS 2014 is a multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, it contains 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. | Provide a detailed description of the following dataset: DTU |
Tanks and Temples | We present a benchmark for image-based 3D reconstruction. The benchmark sequences were acquired outside the lab, in realistic conditions. Ground-truth data was captured using an industrial laser scanner. The benchmark includes both outdoor scenes and indoor environments. High-resolution video sequences are provided as input, supporting the development of novel pipelines that take advantage of video input to increase reconstruction fidelity. We report the performance of many image-based 3D reconstruction pipelines on the new benchmark. The results point to exciting challenges and opportunities for future work.
Paper: Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene
reconstruction. In ACM Transactions on Graphics (TOG), 2017. | Provide a detailed description of the following dataset: Tanks and Temples |
IAPR TC-12 | The image collection of the IAPR TC-12 Benchmark consists of 20,000 still natural images taken from locations around the world and comprising an assorted cross-section of still natural images. This includes pictures of different sports and actions, photographs of people, animals, cities, landscapes, and many other aspects of contemporary life. Each image is associated with a text caption in up to three different languages (English, German and Spanish). | Provide a detailed description of the following dataset: IAPR TC-12 |
QASPER | **QASPER** is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. | Provide a detailed description of the following dataset: QASPER |
im2latex-100k | A prebuilt dataset for OpenAI's task for image-2-latex system. Includes total of ~100k formulas and images splitted into train, validation and test sets. Formulas were parsed from LaTeX sources provided here: http://www.cs.cornell.edu/projects/kddcup/datasets.html(originally from arXiv)
Each image is a PNG image of fixed size. Formula is in black and rest of the image is transparent.
For related tools (eg. tokenizer) check out this repository: https://github.com/Miffyli/im2latex-dataset
For pre-made evaluation scripts and built im2latex system check this repository: https://github.com/harvardnlp/im2markup
**Newlines used in formulas_im2latex.lst are UNIX-style newlines (\n). Reading file with other type of newlines results to slightly wrong amount of lines (104563 instead of 103558), and thus breaks the structure used by this dataset. Python 3.x reads files using newlines of the running system by default, and to avoid this file must be opened with newlines="\n" (eg. open("formulas_im2latex.lst", newline="\n")).** | Provide a detailed description of the following dataset: im2latex-100k |
RLBench | **RLBench** is an ambitious large-scale benchmark and learning environment designed to facilitate research in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning. | Provide a detailed description of the following dataset: RLBench |
MULTEXT-East | The **MULTEXT-East** resources are a multilingual dataset for language engineering research and development. It consists of the (1) MULTEXT-East morphosyntactic specifications, defining categories (parts-of-speech), their morphosyntactic features (attributes and values), and the compact MSD tagset representations; (2) morphosyntactic lexica, (3) the annotated parallel "1984" corpus; and (4) some comparable text and speech corpora. The specifications are available for the following macrolanguages, languages and language varieties: Albanian, Bulgarian, Chechen, Czech, Damaskini, English, Estonian, Hungarian, Macedonian, Persian, Polish, Resian, Romanian, Russian, Serbo-Croatian, Slovak, Slovene, Torlak, and Ukrainian, while the other resources are available for a subset of these languages. | Provide a detailed description of the following dataset: MULTEXT-East |
SketchyCOCO | SketchyCOCO dataset consists of two parts:
**Object-level data**
Object-level data contains $20198(train18869+val1329)$ triplets of {foreground sketch, foreground image, foreground edge map} examples covering 14 classes, $27683(train22171+val5512)$ pairs of {background sketch, background image} examples covering 3 classes.
**Scene-level data**
Scene-level data contains $14081(train 11265 + val 2816)$ pairs of {foreground image&background sketch, scene image} examples, $14081(train 11265 + val 2816)$ pairs of {scene sketch, scene image} examples and the segmentation ground truth for $14081(train 11265 + val 2816)$ scene sketches. Some val scene images come from the train images of the COCO-Stuff dataset for increasing the number of the val images of the SketchyCOCO dataset. | Provide a detailed description of the following dataset: SketchyCOCO |
Scribble | **Scribble** is a new outline dataset consisting of 200 images (150 train, 50 test) for each of 10 classes – basketball, chicken, cookie, cupcake, moon, orange, soccer, strawberry, watermelon and pineapple. All the images have a white background and were collected using search keywords on popular search engines. In each image, we obtain rough
outlines for the image. We find the largest blob in the image after thresholding it into a black and white image. We fill the interior holes of the largest blob and obtain a smooth outline using the SavitzkyGolay filter. | Provide a detailed description of the following dataset: Scribble |
Milling Data Set | Experiments on a metal milling machine for different speeds, feeds, and depth of cut. Records the wear of the milling insert, VB. The data set was provided by the BEST lab at UC Berkeley. | Provide a detailed description of the following dataset: Milling Data Set |
Wiki-Reliability | Wiki-Reliability is the first dataset of English Wikipedia articles annotated with a wide set of content reliability issues. Templates are tags used by expert Wikipedia editors to indicate content issues, such as the presence of "non-neutral point of view" or "contradictory articles", and serve as a strong signal for detecting reliability issues in a revision. We select the 10 most popular reliability-related templates on Wikipedia, and propose an effective method to label almost 1M samples of Wikipedia article revisions as positive or negative with respect to each template. Each positive/negative example in the dataset comes with the full article text and 20 features from the revision's metadata. We provide an overview of the possible downstream tasks enabled by such data, and show that Wiki-Reliability can be used to train large-scale models for content reliability prediction. | Provide a detailed description of the following dataset: Wiki-Reliability |
ExpMRC | **ExpMRC** is a benchmark for the Explainability evaluation of Machine Reading Comprehension. ExpMRC contains four subsets of popular MRC datasets with additionally annotated evidences, including [SQuAD](squad), [CMRC 2018](cmrc-2018), RACE+ (similar to [RACE](race)), and [C3](c3), covering span-extraction and multiple-choice questions MRC tasks in both English and Chinese. | Provide a detailed description of the following dataset: ExpMRC |
DiagSet | **DiagSet** is a histopathological dataset for prostate cancer detection. The proposed dataset consists of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnosis, and 46 scans with diagnosis given independently by a group of histopathologists. | Provide a detailed description of the following dataset: DiagSet |
gComm | **gComm** is a step towards developing a robust platform to foster research in grounded language acquisition in a more challenging and realistic setting. It comprises a 2-D grid environment with a set of agents (a stationary speaker and a mobile listener connected via a communication channel) exposed to a continuous array of tasks in a partially observable setting. The key to solving these tasks lies in agents developing linguistic abilities and utilizing them for efficiently exploring the environment. The speaker and listener have access to information provided in different modalities, i.e. the speaker's input is a natural language instruction that contains the target and task specifications and the listener's input is its grid-view. Each must rely on the other to complete the assigned task, however, the only way they can achieve the same, is to develop and use some form of communication. gComm provides several tools for studying different forms of communication and assessing their generalization. | Provide a detailed description of the following dataset: gComm |
e-ViL | **e-ViL** is a benchmark for explainable vision-language tasks. e-ViL spans across three datasets of human-written NLEs (natural language explanations), and provides a unified evaluation framework that is designed to be re-usable for future works.
This benchmark uses the following datasets: [e-SNLI-VE](e-snli-ve), [VCR](vcr), VQA-X. | Provide a detailed description of the following dataset: e-ViL |
e-SNLI-VE | e-SNLI-VE is a large VL (vision-language) dataset with NLEs (natural language explanations) with over 430k instances for which the explanations rely on the image content. It has been built by merging the explanations from [e-SNLI](e-snli) and the image-sentence pairs from [SNLI-VE](snli-ve). | Provide a detailed description of the following dataset: e-SNLI-VE |
HLGD | The Headline Grouping dataset is a binary classification dataset on pairs of news headline.
For each pair of headline, the binary label indicates whether the two headlines are part of the same group (and describe the same underlying event), or whether they are in distinct groups.
The dataset contains a total of 20k annotated headline pairs, further split in a train, validation and test portions. | Provide a detailed description of the following dataset: HLGD |
CRUW | **CRUW** is a dataset for the radar object detection (ROD) task, which aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. The CRUW dataset has a systematic annotation and evaluation system, which involves camera RGB images and radar RF images, collected in various driving scenarios. | Provide a detailed description of the following dataset: CRUW |
FoodSeg103 | **FoodSeg103** is a new food image dataset containing 7,118 images. Images are annotated with 104 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks. It's provided as a large-scale benchmark for food image segmentation.
Major Challenges:
1. High intra-variance of the same food ingredient with different cooking methods
2. Long-tail distribution
3. Complicated contexts
Image source: [https://arxiv.org/pdf/2105.05409v1.pdf](https://arxiv.org/pdf/2105.05409v1.pdf) | Provide a detailed description of the following dataset: FoodSeg103 |
Kleister NDA | **Kleister NDA** is a dataset for Key Information Extraction (KIE). The dataset contains a mix of scanned and born-digital long formal English-language documents. For this datasets, an NLP system is expected to find or infer various types of entities by employing both textual and structural layout features. The Kleister NDA dataset has 540 Non-disclosure Agreements, with 3,229 unique pages and 2,160 entities to extract. | Provide a detailed description of the following dataset: Kleister NDA |
LabPics | LabPics Chemistry Dataset
Dataset for computer vision for materials segmentation and classification in chemistry labs, medical labs, and any setting where materials are handled inside containers.
The Vector-LabPics dataset comprises 7900 images of materials in various phases and processes within mostly transparent vessels in chemistry labs, medical labs and hospitals, and other environments. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, gel, granular, vapor) . The fill level, labels, corks, and other parts of the vessel are also annotated. The material classes cover the main states of matter, including liquids, solids, vapors, foams, gels, and subcategories like powder, granular, and suspension. Relationships between materials, such as which material is immersed inside other materials, are annotated. The vessel class cover glassware, labware plates, bottles, and any other type of vessel that is used to contain or carry materials. The type of vessel (e.g., syringe, tube, cup, infusion bottle/bag), and the properties of the vessel (transparent, opaque) are annotated. In addition, vessel parts such as corks, labels, spikes, and valves are annotated. Relations and hierarchies between vessels and materials are also annotated, such as which vessel contains which material or which vessels are linked or contain each other. The images were collected from various contributors and covered most aspects of chemistry lab works as well as a variety of other fields where materials are handled in container vessels. Documents specifying annotation formats are available inside the dataset file. Version 1 contain 2200 images with simple instance and semantic annotations, and is relatively simple to use, it is described in the paper "Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set"
Format
The dataset contains annotated images for both material and vessels in chemistry labs, medical labs, and any area where liquids and solids are handled within vessels. There are two levels of annotation for each image. One annotation set for vessels and the second for the material phases inside these vessels. Vessels are defined as any container that can carry materials such as Jars, Erlenmayers, Tubes, Funnels, syringes, IV bags, and any other labware or glassware that can contain or carry materials. Material phases are any material contained within or on the vessel. For example, for two-phase separating liquids, each liquid phase is annotated as one instance. If there is foam above the liquid or a chunk of solid inside the liquid, the foam, liquid, and solid will be annotated as different phases. In addition, vessel parts like cork, labels, and valves are annotated as instances. For each instance, there is a list of all the classes it belongs to, and a list of its property. For vessels, the instance classes are the vessel type (Cup, jar, Separatory-funnel…) and the vessel properties (Transparent, Opaque…). For materials, the classes are the material types ( Liquid, solid, suspension, foam, powder…) and their properties (Scattered, On vessel surface…), and for parts, the part type (cork/label). In addition, the relations between instances are annotated. This includes which material instances are inside which vessels, which vessels are linked to each other or are inside each other (for vessels inside other vessels), and which material phase is immersed inside another material phase. In addition to instance segmentation maps, the dataset also includes semantic segmentation maps that give each pixel in the image all the classes to which it belongs. In other words, for each class (Liquid, Solid, Vessel, Foam), there is a map of all the regions in the image belonging to this class. Note that every pixel and every instance can have several classes. In addition, instances often overlap, like in the case of material inside the vessel, vessel inside the vessel, and material phase immersed inside other material (like solid inside liquid). | Provide a detailed description of the following dataset: LabPics |
TextOCR | **TextOCR** is a dataset to benchmark text recognition on arbitrary shaped scene-text. TextOCR requires models to perform text-recognition on arbitrary shaped scene-text present on natural images. TextOCR provides ~1M high quality word annotations on TextVQA images allowing application of end-to-end reasoning on downstream tasks such as visual question answering or image captioning.
Dataset statistics:
- 28,134 natural images from TextVQA
- 903,069 annotated scene-text words
- 32 words per image on average | Provide a detailed description of the following dataset: TextOCR |
Scientific statement classification dataset from arXMLiv 08.2018 | This resource contains 10.5 million paragraphs with associated statement labels, realized as one paragraph per file, one sentence per line. Each file is placed in a subdirectory named after its annotated class. The statements were extracted from author-annotated environments, where we only selected the first paragraph,immediately following the heading. Headings include both structural sections (e.g. Introduction), as well as scholarly statement annotations, (e.g. Definition, Proof, Remark).
The annotated statement dataset is derived from arXMLiv, a machine-readable HTML5 representation of the arXiv corpus of scientific articles.
### Examples
Definition with math lexemes (main data, single sentence, linebreaks for readability):
```
a directed quantum turing automaton is a quadruple
italic_T RELOP_equals OPEN_( caligraphic_H PUNCT_, caligraphic_K PUNCT_, caligraphic_L PUNCT_, italic_tau CLOSE_) PUNCT_,
where
caligraphic_H caligraphic_K and caligraphic_L
are finite dimensional hilbert spaces over the complex field blackboard_C and
italic_tau METARELOP_colon caligraphic_H MULOP_tensor_product caligraphic_K ARROW_rightarrow
caligraphic_H MULOP_tensor_product caligraphic_L
is an isometry in fdhilb
```
source: `definition/1e4a1aea317bbf363c5314fb25eaf72c8a350a1007bb8aafc542e188405b93d5.txt`
Same definition without math lexemes (nomath data, single sentence, linebreaks for readability):
```
a directed quantum turing automaton is a quadruple
where and are finite dimensional hilbert spaces over the complex field and
is an isometry in fdhilb
```
nomath source: `definition/35b170bae4259a5c430846116142d4e4a45097e52daf818b78ea378d94d14a21.txt` | Provide a detailed description of the following dataset: Scientific statement classification dataset from arXMLiv 08.2018 |
arXMLiv:08.2018 | This is a second public release of the arXMLiv dataset generated by the KWARC research group. It contains 1,232,186 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources. A 13% increase in available articles over the 08.2017 release.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
derivative word embeddings and a token model are available separately [here](https://sigmathling.kwarc.info/resources/arxmliv-embeddings-082018/) | Provide a detailed description of the following dataset: arXMLiv:08.2018 |
Extreme Countix-AV | * 214 videos under various extreme sight conditions for audiovisual repetition counting
* 7 vision challenges: camera viewpoint changes, cluttered background, low illumination, fast motion, disappearing activity, scale variation, low resolution | Provide a detailed description of the following dataset: Extreme Countix-AV |
Ninapro DB5 | The 5th Ninapro database includes 10 intact subjects recorded with two Thalmic Myo (https://www.myo.com/) armbands.
The database can be used to test the Myo armbands separately as well.
The 5th Ninapro database is thoroughly described in the paper: ["Stefano Pizzolato, Luca Tagliapietra, Matteo Cognolato, Monica Reggiani, Henning Müller, Manfredo Atzori, Comparison of six electromyography acquisition setups on hand movement classification tasks, PLOS One, 2017"](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186132)
### Acquisition Protocol
The subjects have to repeat several movements represented by movies that are shown on the screen of a laptop.
The experiment is divided in three exercises:
1. Basic movements of the fingers
2. Isometric, isotonic hand configurations and basic wrist movements
3. Grasping and functional movements
During the acquisition, the subjects were asked to repeat the movements with the right hand. Each movement repetition lasted 5 seconds and was followed by 3 seconds of rest.
The protocol includes 6 repetitions of 52 different movements (plus rest) performed by 10 intact subjects. The movements were selected from the hand taxonomy as well as from hand robotics literature.
### Acquisition Setup
The muscular activity is gathered using 2 Thalmic Myo armbands. The database can be used to test the Myo armbands separately as well.
The subjects in this database wore two Myo armbands one next to the other, including 16 active single–differential wireless electrodes. The top Myo armband is placed closed to the elbow with the first sensor placed on the radio humeral joint, as in the standard Ninapro configuration for the equally spaced electrodes; the second Myo armband is placed just after the first, nearer to the hand, tilted of 22.5 degrees. This configuration provides an extended uniform muscle mapping at an extremely affordable cost. The Myo sensors do not require the arm to be shaved and after few minutes the armband tighten very firmly to the arm of the subject.
The sEMG signals are sampled at a rate of 200 Hz. | Provide a detailed description of the following dataset: Ninapro DB5 |
Copel-AMR | This dataset contains 12,500 meter images acquired in the field by the employees of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units, across 395 cities and 1,113 locations (i.e., districts, villages and settlements), located in the Brazilian state of Paraná.
Copel-AMR is composed of images captured in unconstrained scenarios, which typically include blur (due to camera motion), dirt, scale variations, in-plane and out-of-plane rotations, reflections, shadows, and occlusions. In 2,500 images (i.e., 20% of the dataset), it is not even possible to perform the meter reading due to occlusions or faulty meters.
The images have a resolution of 480×640 or 640×480 pixels, depending on the orientation in which they were taken. Considering that the meter is operational and that there are no occlusions, these resolutions are enough for the meter reading to be legible.
The dataset was randomly split as follows: 5,000 images for training, 5,000 images for testing and 2,500 images for validation, following the split protocol (i.e., 40%/40%/20%) used in the UFPR-AMR dataset. For reproducibility purposes, the subsets generated are explicitly available along with the Copel-AMR dataset.
For each image in our dataset, we manually labeled the meter reading, the position (x, y) of each of the four corners of the counter, and a bounding box (x, y, w, h) for each digit. Corner annotations – which can be converted to a bounding box – enable the counter to be rectified, while bounding boxes enable the training of object detectors. | Provide a detailed description of the following dataset: Copel-AMR |
BRUSH | The BRUSH dataset (BRown University Stylus Handwriting) contains 27,649 online handwriting samples from a total of 170 writers. Every sequence is labeled with intended characters such that dataset users can identify to which character a point in a sequence corresponds. The dataset was introduced in the paper "Generating Handwriting via Decoupled Style Descriptors" by Atsunobu Kotani, Stefanie Tellex, James Tompkin from Brown University, presented at European Conference on Computer Vision (ECCV) 2020.
##### Terms of Use
The BRUSH dataset may only be used for non-commercial research purposes. Anyone wanting to use it for other purposes should contact Prof. James Tompkin. If you publish materials based on this database, we request that you please include a reference to our paper.
```
@inproceedings{kotani2020generating,
title={Generating Handwriting via Decoupled Style Descriptors},
author={Kotani, Atsunobu and Tellex, Stefanie and Tompkin, James},
booktitle={European Conference on Computer Vision},
pages={764--780},
year={2020},
organization={Springer}
}
``` | Provide a detailed description of the following dataset: BRUSH |
UFPR-ALPR | This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.
The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.
We collected 1,500 images with each camera, divided as follows:
- 900 of cars with gray license plates;
- 300 of cars with red license plates;
- 300 of motorcycles with gray license plates.
The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters. | Provide a detailed description of the following dataset: UFPR-ALPR |
SSIG-SegPlate | This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015.
The images of the dataset were acquired with a digital camera in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The average size of each file is 4.08 Megabytes (a total of 8.60 Gigabytes for the entire dataset). In addition, since there are some approaches that track the car to utilize redundant information to improve the recognition results, we decided to make a dataset with multiples frames per car. In this dataset, there are, on average, 19.80 image frames per vehicle (with a standard deviation of 4.14). | Provide a detailed description of the following dataset: SSIG-SegPlate |
Caltech Cars | The Caltech Cars dataset consists of 126 rear-view photographs captured within parking lots. These images possess a resolution of 896 × 592 pixels, featuring a solitary vehicle as the primary subject. The acquisitions were made during daylight hours employing a handheld camera at roughly equivalent distances for all instances.
"The dataset was presented in a PhD thesis titled 'Unsupervised learning of models for object recognition.'" | Provide a detailed description of the following dataset: Caltech Cars |
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