Omkar2118/datasets huggingface dataset:
OPA
Full name: Object Placement Assessment

Object-Placement-Assessment (OPA) is a task consisting on verifying whether a composite image is plausible in terms of the object placement. The foreground object should be placed at a reasonable location on the background considering location, size, occlusion, semantics, and etc.

**OPA** is a synthesised dataset for Object Placement Assessment based on COCO dataset. The authors selected unoccluded objects from multiple categories as our candidate foreground objects. The foreground objects are pasted on their compatible background images with random sizes and locations to form composite images, which are sent to human annotators for rationality labeling.

Introducing paper: OPA: Object Placement Assessment Dataset
MineRL BASALT

**MineRL BASALT** is an RL competition on solving human-judged tasks. The tasks in this competition do not have a pre-defined reward function: the goal is to produce trajectories that are judged by real humans to be effective at solving a given task.

Introducing paper: The MineRL BASALT Competition on Learning from Human Feedback
SBU-WSD-Corpus

**SBU-WSD-Corpus** is a corpus for Persian Word Sense Disambiguation (WSD). It is manually annotated with senses from the Persian WordNet (FarsNet) sense inventory. SBU-WSD-Corpus consists of 19 Persian documents in different domains such as Sports, Science, Arts, etc. It includes 5892 content words of Persian running text and 3371 manually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122 adverbs).

Introducing paper: Persian-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation
VinDr-RibCXR

**VinDr-RibCXR** is a benchmark dataset for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts.

Introducing paper: VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
Disaster

**Disaster** is a dataset that contains images collected from various sources for three different disasters: fire, water and land. Besides this, it also contains images for various damaged infrastructure due to natural or man made calamities and damaged human due to war or accidents.

There are 13,720 manually annotated images in this dataset, each image is annotated by three individuals. The authors are also providing discriminating image class information annotated manually with bounding box for a set of 200 test images. Images are collected from different news portals, social media, and standard datasets made available by other researchers.

Introducing paper: A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model
Google Landmarks

The **Google Landmarks** dataset contains 1,060,709 images from 12,894 landmarks, and 111,036 additional query images. The images in the dataset are captured at various locations in the world, and each image is associated with a GPS coordinate. This dataset is used to train and evaluate large-scale image retrieval models.

Introducing paper: Large-Scale Image Retrieval with Attentive Deep Local Features
KiTS19
Full name: The 2019 Kidney and Kidney Tumor Segmentation Challenge

The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy.

[The 2021 Kidney and Kidney Tumor Segmentation Challenge](https://kits21.kits-challenge.org/)

[The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://arxiv.org/abs/1912.01054)