Omkar2118/datasets huggingface dataset:
CrossMoDA
Full name: Cross-Modality Domain Adaptation

****CrossMoDA** is a large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging.

Introducing paper: CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
DADA-seg

DADA-seg is a pixel-wise annotated accident dataset, which contains a variety of critical scenarios from traffic accidents. It is used for semantic segmentation.

Introducing paper: Exploring Event-driven Dynamic Context for Accident Scene Segmentation
MyoPS

**MyoPS** is a dataset for myocardial pathology segmentation combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segment

Introducing paper: MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
3D-BSLS-6D
Full name: 3D scans of Bins by Structured-Light Scanner for 6D pose estimation

Dataset consist of both real captures from Photoneo PhoXi structured light scanner devices annotated by hand and synthetic samples produced by custom generator. In comparison with existing datasets for 6D pose estimation, some notable differences include:

* most of the captured bins are texture-less, made from uniform, single-colored materials,
* all bins are of cuboid shape with different proportions. Compared to objects with complex geometry, bins consist of flat faces with edges, which are not guaranteed to be seen in the capture due to occlusion. Surface models of these bins are not provided, just their approximate bounding boxes,
* PhoXi scanner provides high-resolution 3D geometry data, but no RGB data, with a rough and noisy gray-scale intensity image being the closest equivalent,
* captures come from different devices with various intrinsic camera parameters.  3D point clouds contain these parameters implicitly as opposed to RGBD images.

Due to its currently limited size, we recommend cross-validation instead of an explicit train-validation split. We plan to add more samples into the dataset. NOTE: Annotation files with suffixes _bad, _ish or _catastrophic should be ignored. It was not possible to annotate them correctly with our current toolset.

Introducing paper: Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans
FR-FS
Full name: Fall Recognition in Figure Skating

The FR-FS dataset contains 417 videos collected from FIV dataset and Pingchang 2018 Winter Olympic Games. FR-FS contains the critical movements of the athlete’s take-off, rotation, and landing. Among them, 276 are smooth landing videos, and 141 are fall videos.
To test the generalization performance of our proposed model, we randomly select 50% of the videos from the fall and landing videos as the training set and the testing set.

Introducing paper: TSA-Net: Tube Self-Attention Network for Action Quality Assessment
results-A

The results-A dataset is a dataset consisting of  22 infrared images commonly used for testing performance of Infrared Image Super-Resolution models.
results-C

The results-C dataset is a dataset consisting of  22 infrared images commonly used for testing performance of Infrared Image Super-Resolution models.
GrailQA
Full name: Strongly Generalizable Question Answering

GrailQA is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.

Introducing paper: Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
EurekaAlert
Full name: Eureka Alert

This dataset contains around 5000 scholarly articles and their corresponding easy summary from eureka alert blog, the dataset can be used for the combined task of summarization and simplification.

Introducing paper: HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture
UAV-VeID

1. Data Collection
We simulate real scenarios as much as possible during the UAV videos collection. 
Specifically, UAV videos are collected from different locations with distinct backgrounds and lighting conditions, e.g., including highways, urban road intersections, parking lots, etc. 
For vehicles at parking lots, we adopt various UAV sport modes such as cruising and rotating to record vehicles. 
This strategy introduces viewpoint and scale changes, as well as partial occlusions to images of the same vehicle. 
For moving vehicles, we use two UAVs to simultaneously shoot videos from different viewpoints and heights. 
This strategy introduces viewpoint, scale, and background changes. 
The flying height of UAVs ranges from 15 to 60 meters, leading to different scales of vehicle images. 
The vertical angle of UAV camera ranges from 40 to 80 degrees, which leads to different viewpoints of vehicle images. 
The videos are recorded at 30 frames per second (fps), with the resolution of 2704 × 1520 pixels and 4096 × 2160 pixels, respectively. 
The UAV-VeID is constructed from 80 video sequences selected from raw UAV videos.

2. Annotation
We annotate vehicles from collected videos to construct the UAV-VeID. 
In each video clip, 1 video frame is sampled every one second to construct a video frame dataset. 
The dataset annotation is hence conducted based on those sample video frames.
To finish the vehicle annotation, 6 domain experts are involved to manually locate and annotate the identities of vehicles from each video frame.
The data annotation procedure takes 1000 man-hours and finally results in a dataset containing 41,917 vehicle bounding boxes of 4601 vehicles. 
Each vehicle is annotated by at least two bounding boxes. 

3. Dataset partition
The UAV-VeID dataset is split into the training set, validation set, and testing set, among which the training set contains 18,709 images with 1,797 IDs, the validation set contains 4,150 images with 596 IDs, and the testing set contains 19,058 images with 2,208 IDs. 
The validation set is further divided into a query set ("val_q_label.txt" 3,554 images) and a gallery set ("val_g_label.txt" 596 images). 
The testing set is further divided into a query set ("test_q_label.txt" 16,850 images) and a gallery set ("test_g_label.txt" 2,208 images). 

4. Download
Please sign the Agreement(UAV-VeID_AGREEMENT.pdf) and thereby agrees to observe the restrictions listed in this document. 
After filling it, please send the electrical version to us. After confirming your information, we will send the download link and password to you via Email.

5. Contact
Shangzhi Teng, Email: tengshangzhi@126.com
ITB
Full name: Informative Tracking Benchmark

**Informative Tracking Benchmark** (**ITB**) is a small and informative tracking benchmark with 7% out of 1.2 M frames of existing and newly collected datasets, which enables efficient evaluation while ensuring effectiveness. Specifically, the authors designed a quality assessment mechanism to select the most informative sequences from existing benchmarks taking into account 1) challenging level, 2) discriminative strength, 3) and density of appearance variations. Furthermore, they collect additional sequences to ensure the diversity and balance of tracking scenarios, leading to a total of 20 sequences for each scenario.

Introducing paper: An Informative Tracking Benchmark