Omkar2118/datasets huggingface dataset:
Recipe1M+
Full name: Recipe1M+

**Recipe1M+** is a dataset which contains one million structured cooking recipes with 13M associated images.

Source: [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](http://im2recipe.csail.mit.edu/)
Image Source: [http://im2recipe.csail.mit.edu/](http://im2recipe.csail.mit.edu/)

Introducing paper: Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
DARPA
Full name: DARPA

Darpa is a dataset consisting of communications between source IPs and destination IPs. This dataset contains different attacks between IPs.

Source: [dynnode2vec: Scalable Dynamic Network Embedding](https://arxiv.org/abs/1812.02356)
Image Source: [https://archive.ll.mit.edu/ideval/files/1999_DARPA_EvaulationSumPlans.pdf](https://archive.ll.mit.edu/ideval/files/1999_DARPA_EvaulationSumPlans.pdf)

Introducing paper: Results of the DARPA 1998 Offline Intrusion Detection Evaluation
TAC 2010
Full name: TAC 2010

**TAC 2010** is a dataset for summarization that consists of 44 topics, each of which is associated with a set of 10 documents. The test dataset is composed of approximately 44 topics, divided into five categories: Accidents and Natural Disasters, Attacks, Health and Safety, Endangered Resources, Investigations and Trials.

Source: [Better Summarization Evaluation with Word Embeddings for ROUGE](https://arxiv.org/abs/1508.06034)
Image Source: [https://tac.nist.gov//2010/Summarization/Guided-Summ.2010.guidelines.html](https://tac.nist.gov//2010/Summarization/Guided-Summ.2010.guidelines.html)
TUM-GAID
Full name: TUM-GAID

**TUM-GAID** (TUM Gait from Audio, Image and Depth) collects 305 subjects performing two walking trajectories in an indoor environment. The first trajectory is traversed from left to right and the second one from right to left. Two recording sessions were performed, one in January, where subjects wore heavy jackets and mostly winter boots, and another one in April, where subjects wore lighter clothes. The action is captured by a Microsoft Kinect sensor which provides a video stream with a resolution of 640×480 pixels and a frame rate around 30 FPS.

Source: [Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs](https://arxiv.org/abs/1808.00286)
Image Source: [https://www.ei.tum.de/mmk/verschiedenes/tum-gaid-database/](https://www.ei.tum.de/mmk/verschiedenes/tum-gaid-database/)

Introducing paper: The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits
DUC 2005
Full name: DUC 2005

The **DUC 2005** data set is a dataset for summarization which consists of 50 document collections of 25 documents each; each document collection includes a human-written query. Each document collection additionally has five human-written “reference” summaries (250 words long, each) that serve as the gold standard

Source: [Search-based Structured Prediction](https://arxiv.org/abs/0907.0786)
Image Source: [https://duc.nist.gov/duc2005/tasks.html](https://duc.nist.gov/duc2005/tasks.html)
NIST SD 19
Full name: NIST Special Dataset 19

NIST Special Database 19 contains NIST's entire corpus of training materials for handprinted document and character recognition. It publishes Handprinted Sample Forms from 3600 writers, 810,000 character images isolated from their forms, ground truth classifications for those images, reference forms for further data collection, and software utilities for image management and handling.

Source: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
Image Source: [https://www.nist.gov/srd/nist-special-database-19](https://www.nist.gov/srd/nist-special-database-19)
PRImA
Full name: PRImA

The Prima head pose dataset consists of 2790 images of 15 persons recorded twice. Pitch values lie in the interval [−60∘,60∘], and yaw values lie in the interval [−90∘,90∘] with a 15∘ step. Thus, there are 93 poses available for each person. All the recordings were achieved with the same background. One interesting feature of this dataset is the pose space is uniformly sampled. The dataset is annotated such that a face bounding box (manually annotated) and the corresponding yaw and pitch angle values are provided for each sample.

Source: [Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions](https://arxiv.org/abs/1603.09732)
Image Source: [http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html](http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html)
CASIA V2
Full name: CASIA V2

**CASIA V2** is a dataset for forgery classification. It contains 4795 images, 1701 authentic and 3274 forged.

Source: [Copy-Move Forgery Classification via Unsupervised Domain Adaptation](https://arxiv.org/abs/1911.07932)
Image Source: [https://www.mdpi.com/1099-4300/21/4/371](https://www.mdpi.com/1099-4300/21/4/371)

Introducing paper: CASIA Image Tampering Detection Evaluation Database
KTH Multiview Football II
Full name: KTH Multiview Football II

KTI Multiview Football II consists of images of professional footballers during a match of the Allsvenskan league. It consists of two parts: one with ground truth pose in 2D and one with ground truth pose in both 2D and 3D. The 3D dataset has 800 time frames, captured from 3 views (2400 images). Views are calibrated and synchronized. 3D ground truth pose and orthographic camera matrices are provided for each frame. There are 14 annotated joints. Lastly, there are 2 different players and two sequences per player.

Source: [http://www.csc.kth.se/~vahidk/football_data.html](http://www.csc.kth.se/~vahidk/football_data.html)
Image Source: [http://www.csc.kth.se/cvap/cvg/?page=footballdataset2](http://www.csc.kth.se/cvap/cvg/?page=footballdataset2)
KTH Multiview Football I
Full name: KTH Multiview Football I

KTI Multiview Football I is a dataset of football players with annotated joints that can be used for multi-view reconstruction. The dataset includes 771 images of football players, images taken from 3 views at 257 time instances, and 14 annotated body joints.

Source: [http://www.csc.kth.se/~vahidk/football_data.html](http://www.csc.kth.se/~vahidk/football_data.html)
Image Source: [http://www.csc.kth.se/~vahidk/football_data.html](http://www.csc.kth.se/~vahidk/football_data.html)
TUM monoVO
Full name: TUM monoVO

**TUM monoVO** is a dataset for evaluating the tracking accuracy of monocular Visual Odometry (VO) and SLAM methods. It contains 50 real-world sequences comprising over 100 minutes of video, recorded across different environments – ranging from narrow indoor corridors to wide outdoor scenes.
All sequences contain mostly exploring camera motion, starting and ending at the same position: this allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground-truth for the full sequence.
In contrast to existing datasets, all sequences are photometrically calibrated: the dataset creators provide the exposure times for each frame as reported by the sensor, the camera response function and the lens attenuation factors (vignetting).

Source: [https://vision.in.tum.de/data/datasets/mono-dataset](https://vision.in.tum.de/data/datasets/mono-dataset)
Image Source: [https://vision.in.tum.de/data/datasets/mono-dataset](https://vision.in.tum.de/data/datasets/mono-dataset)

Introducing paper: A Photometrically Calibrated Benchmark For Monocular Visual Odometry
ICL-NUIM
Full name: ICL-NUIM

The **ICL-NUIM** dataset aims at benchmarking RGB-D, Visual Odometry and SLAM algorithms. Two different scenes (the living room and the office room scene) are provided with ground truth. Living room has 3D surface ground truth together with the depth-maps as well as camera poses and as a result perfectly suits not just for benchmarking camera trajectory but also reconstruction. Office room scene comes with only trajectory data and does not have any explicit 3D model with it.

All data is compatible with the evaluation tools available for the TUM RGB-D dataset, and if your system can take TUM RGB-D format PNGs as input, the authors’ TUM RGB-D Compatible data will also work (given the correct camera parameters).

Source: [https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html](https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html)
Image Source: [https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html](https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html)
EuRoC MAV
Full name: EuRoC MAV

**EuRoC MAV** is a visual-inertial datasets collected on-board a Micro Aerial Vehicle (MAV). The dataset contains stereo images, synchronized IMU measurements, and accurate motion and structure ground-truth. The datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data

Source: [https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets)
Image Source: [https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets)
Sugar Beets 2016
Full name: Sugar Beets 2016

**Sugar Beets 2016** is a robot dataset for plant classification as well as localization and mapping that covers the relevant stages for robotic intervention and weed control. It contains around 5TB of data recorded from a robot with a 4-channel multi-spectral camera and a RGB-D sensor to capture detailed information about the plantation.

Source: [https://www.ipb.uni-bonn.de/data/sugarbeets2016/](https://www.ipb.uni-bonn.de/data/sugarbeets2016/)
Image Source: [https://www.ipb.uni-bonn.de/data/sugarbeets2016/](https://www.ipb.uni-bonn.de/data/sugarbeets2016/)
HDM05
Full name: HDM05

**HDM05** is a MoCap (motion capture) dataset. It contains more than three hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format. HDM05 contains almost 2337 sequences with 130 motion classes performed by 5 different actors.

Source: [http://resources.mpi-inf.mpg.de/HDM05/](http://resources.mpi-inf.mpg.de/HDM05/)
Image Source: [https://arxiv.org/pdf/1908.05750.pdf](https://arxiv.org/pdf/1908.05750.pdf)
USYD CAMPUS
Full name: USYD CAMPUS

**USYD CAMPUS** is a driving dataset collected by Zhou et al at the University of Sydney (USyd) campus and surroundings. This USYD Campus Dataset contains more than 60 weeks of drives and is continuously updated. It includes multiple sensor modalities (camera, lidar, GPS, IMU, wheel encoder, steering angle, etc.) and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes.

Source: [http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/](http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/)
Image Source: [http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/](http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/)
TRECVID
Full name: TRECVID

**TRECVID** is a yearly set of competitions centered on video retrieval and indexing, hosting a variety of video data sets.

Source: [YouTube-BoundingBoxes: A Large High-PrecisionHuman-Annotated Data Set for Object Detection in Video](https://arxiv.org/abs/1702.00824)
Image Source: [https://www-nlpir.nist.gov/projects/tv2016/tv2016.html](https://www-nlpir.nist.gov/projects/tv2016/tv2016.html)
Partial-iLIDS
Full name: Partial-iLIDS

Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage.

Source: [Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification](https://arxiv.org/abs/1904.04975)
Image Source: [https://arxiv.org/pdf/1904.04975.pdf](https://arxiv.org/pdf/1904.04975.pdf)

Introducing paper: Person re-identification by probabilistic relative distance comparison
Oxford Town Center
Full name: Oxford Town Center

The **Oxford Town Center** dataset is a 5-minute video with 7500 frames annotated, which is divided into 6500 for training and 1000 for testing data for pedestrian detection. The data was recorded from a CCTV camera in Oxford for research and development into activity and face recognition.

Source: [LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning](https://arxiv.org/abs/1606.08998)
Image Source: [https://megapixels.cc/oxford_town_centre/](https://megapixels.cc/oxford_town_centre/)

Introducing paper: Stable multi-target tracking in real-time surveillance video