Omkar2118/datasets huggingface dataset:
Airport
Full name: Airport

The **Airport** dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras.

Source: [An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification](https://arxiv.org/abs/1805.06086)
Image Source: [http://www.northeastern.edu/alert/transitioning-technology/alert-datasets/alert-airport-re-identification-dataset/](http://www.northeastern.edu/alert/transitioning-technology/alert-datasets/alert-airport-re-identification-dataset/)

Introducing paper: A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Musk v1
Full name: Musk v1

The Musk dataset describes a set of molecules, and the objective is to detect musks from non-musks. This dataset describes a set of 92 molecules of which 47 are judged by human experts to be musks and the remaining 45 molecules are judged to be non-musks. There are 166 features available that describe the molecules based on the shape of the molecule.

Source: [Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders](https://arxiv.org/abs/1811.04576)

Introducing paper: Solving the multiple instance problem with axis-parallel rectangles
Musk v2
Full name: Musk v2

The Musk2 dataset is a set of 102 molecules of which 39 are judged by human experts to be musks and the remaining 63 molecules are judged to be non-musks. Each instance corresponds to a possible configuration of a molecule. The 166 features that describe these molecules depend upon the exact shape, or conformation, of the molecule.

Source: [Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data](https://arxiv.org/abs/1603.01901)

Introducing paper: Solving the multiple instance problem with axis-parallel rectangles
RMRC 2014
Full name: RMRC 2014

The **RMRC 2014** indoor dataset is a dataset for indoor semantic segmentation. It employs the NYU Depth V2 and Sun3D datasets to define the training set. The test data consists of newly acquired images.

Source: [https://cs.nyu.edu/~silberman/rmrc2014/indoor.php](https://cs.nyu.edu/~silberman/rmrc2014/indoor.php)
Image Source: [https://cs.nyu.edu/~silberman/rmrc2014/indoor.php](https://cs.nyu.edu/~silberman/rmrc2014/indoor.php)

Introducing paper: Indoor Segmentation and Support Inference from RGBD Images
Middlebury 2001
Full name: Middlebury 2001

The **Middlebury 2001** is a stereo dataset of indoor scenes with multiple handcrafted layouts.

Source: [https://vision.middlebury.edu/stereo/data/scenes2001/](https://vision.middlebury.edu/stereo/data/scenes2001/)
Image Source: [https://vision.middlebury.edu/stereo/data/scenes2001/](https://vision.middlebury.edu/stereo/data/scenes2001/)

Introducing paper: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Middlebury 2006
Full name: Middlebury 2006

The **Middlebury 2006** is a stereo dataset of indoor scenes with multiple handcrafted layouts.

Source: [https://vision.middlebury.edu/stereo/data/scenes2006/](https://vision.middlebury.edu/stereo/data/scenes2006/)
Image Source: [https://vision.middlebury.edu/stereo/data/scenes2006/](https://vision.middlebury.edu/stereo/data/scenes2006/)

Introducing paper: Evaluation of Cost Functions for Stereo Matching
DukeMTMC-attribute
Full name: DukeMTMC-attribute

The images in **DukeMTMC-attribute** dataset comes from Duke University. There are 1812 identities and 34183 annotated bounding boxes in the DukeMTMC-attribute dataset. This dataset contains 702 identities for training and 1110 identities for testing, corresponding to 16522 and 17661 images respectively. The attributes are annotated in the identity level, every image in this dataset is annotated with 23 attributes.

**NOTE**: This dataset [has been retracted](https://exposing.ai/duke_mtmc/).

Source: [Pedestrian Attribute Recognition: A Survey](https://arxiv.org/abs/1901.07474)
Image Source: [http://irip.buaa.edu.cn/mars_duke_attributes/index.html](http://irip.buaa.edu.cn/mars_duke_attributes/index.html)

Introducing paper: Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
Occluded REID
Full name: Occluded REID

**Occluded REID** is an occluded person dataset captured by mobile cameras, consisting of 2,000 images of 200 occluded persons (see Fig. (c)). Each identity has 5 full-body person images and 5 occluded person images with different types of occlusion.

Source: [Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification](https://arxiv.org/abs/1904.04975)
Image Source: [https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets](https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets)

Introducing paper: Occluded Person Re-identification
NYU Hand
Full name: NYU Hand

The **NYU Hand** pose dataset contains 8252 test-set and 72757 training-set frames of captured RGBD data with ground-truth hand-pose information. For each frame, the RGBD data from 3 Kinects is provided: a frontal view and 2 side views. The training set contains samples from a single user only (Jonathan Tompson), while the test set contains samples from two users (Murphy Stein and Jonathan Tompson). A synthetic re-creation (rendering) of the hand pose is also provided for each view.

Source: [https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm](https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm)
Image Source: [https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm](https://jonathantompson.github.io/NYU_Hand_Pose_Dataset.htm)
Middlebury 2005
Full name: Middlebury 2005

**Middlebury 2005** is a stereo dataset of indoor scenes.

Source: [https://vision.middlebury.edu/stereo/data/scenes2005/](https://vision.middlebury.edu/stereo/data/scenes2005/)
Image Source: [https://vision.middlebury.edu/stereo/data/scenes2005/](https://vision.middlebury.edu/stereo/data/scenes2005/)

Introducing paper: Learning Conditional Random Fields for Stereo
Middlebury MVS
Full name: Middlebury MVS

**Middlebury MVS** is the earliest MVS dataset for multi-view stereo network evaluation. It contains two indoor objects with low-resolution (640 × 480) images and calibrated cameras.

Source: [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://arxiv.org/abs/1911.10127)
Image Source: [https://vision.middlebury.edu/mview/data/](https://vision.middlebury.edu/mview/data/)

Introducing paper: A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Middlebury 2003
Full name: Middlebury 2003

**Middlebury 2003** is a stereo dataset for indoor scenes.

Source: [https://vision.middlebury.edu/stereo/data/scenes2003/](https://vision.middlebury.edu/stereo/data/scenes2003/)
Image Source: [https://vision.middlebury.edu/stereo/data/scenes2003/](https://vision.middlebury.edu/stereo/data/scenes2003/)

Introducing paper: High-Accuracy Stereo Depth Maps Using Structured Light
OpeReid
Full name: OpeReid

The **OpeReid** dataset is a person re-identification dataset that consists of 7,413 images of 200 persons.

Source: [Scalable Metric Learning via Weighted Approximate Rank Component Analysis](https://arxiv.org/abs/1603.00370)

Introducing paper: Open-set Person Re-identification
Market1501-Attributes
Full name: Market1501-Attributes

The **Market1501-Attributes** dataset is built from the Market1501 dataset. Market1501 Attribute is an augmentation of this dataset with 28 hand annotated attributes, such as gender, age, sleeve length, flags for items carried as well as upper clothes colors and lower clothes colors.

Source: [Color inference from semantic labeling for person search in videos](https://arxiv.org/abs/1911.13114)
Image Source: [https://github.com/vana77/Market-1501_Attribute](https://github.com/vana77/Market-1501_Attribute)

Introducing paper: Improving Person Re-identification by Attribute and Identity Learning