Omkar2118/datasets huggingface dataset:
FAKBAT

The Freebase Annotations of TREC KBA 2014 Stream Corpus with Timestamps (**FAKBAT**) is an extension of the FAKBA1 dataset that contains entity age and entity timestamp. It comprises roughly 1.2 billion timestamped documents from global public news wires, blogs, forums, and shortened links shared on social media. It spans 572 days (October 7, 2011–May 1, 2013).

Source: [https://arxiv.org/pdf/1701.04039.pdf](https://arxiv.org/pdf/1701.04039.pdf)

Introducing paper: The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams
Fakeddit

**Fakeddit** is a novel multimodal dataset for fake news detection consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision.

Source: [https://fakeddit.netlify.app/](https://fakeddit.netlify.app/)

Introducing paper: r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
Fake News Filipino Dataset

Expertly-curated benchmark dataset for fake news detection in Filipino.

Source: [Localization of Fake News Detection via Multitask Transfer Learning](/paper/localization-of-fake-news-detection-via)

Introducing paper: Localization of Fake News Detection via Multitask Transfer Learning
FPDS
Full name: Fallen People Data Set

A benchmark for detecting fallen people lying on the floor. It consists of 6982 images, with a total of 5023 falls and 2275 non falls corresponding to people in conventional situations (standing up, sitting, lying on the sofa or bed, walking, etc). Almost all the images have been captured in indoor environments with very different situations: variation of poses and sizes, occlusions, lighting changes, etc.

Source: [FPDS](http://agamenon.tsc.uah.es/Investigacion/gram/papers/fall_detection/)

Introducing paper: Fast and Robust Detection of Fallen People from a Mobile Robot
FAS100K

**FAS100K** is a large-scale visual localization dataset. This dataset is comprised of two traverses of 238 and 130 kms respectively where the latter is a partial repeat of the former. The data was collected using stereo cameras in Australia under sunny day conditions. It covers a variety of road and environment types including urban and rural areas. The raw image data from one of the cameras streaming at 5 Hz constitutes 63,650 and 34,497 image frames for the two traverses respectively.

Source: [https://arxiv.org/pdf/2001.08434.pdf](https://arxiv.org/pdf/2001.08434.pdf)

Introducing paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Fashion 144K

**Fashion 144K** is a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information.

Source: [Neuroaesthetics in Fashion: Modeling the Perception of Fashionability](/paper/neuroaesthetics-in-fashion-modeling-the-1)

Introducing paper: Neuroaesthetics in Fashion: Modeling the Perception of Fashionability
Fashion IQ

Fashion IQ support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images.

Source: [Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback](/paper/the-fashion-iq-dataset-retrieving-images-by)

Introducing paper: Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
Fashionpedia

Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. 

Source: [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](/paper/fashionpedia-ontology-segmentation-and-an)

Introducing paper: Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
FAT
Full name: Falling Things

Falling Things (FAT) is a dataset for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. It consists of generated photorealistic images with accurate 3D pose annotations for all objects in 60k images.

The 60k annotated photos of 21 household objects are taken from the YCB objects set. For each image, the dataset contains the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects.

Source: [Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation](/paper/falling-things-a-synthetic-dataset-for-3d)

Introducing paper: Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation
FB15k-237-low

The **FB15k-237-low** dataset is a variation of the FB15k-237 dataset where relations with a low number of triplets are kept.

Source: [https://arxiv.org/pdf/1911.03091.pdf](https://arxiv.org/pdf/1911.03091.pdf)

Introducing paper: Relation Adversarial Network for Low Resource Knowledge Graph Completion
FCDB
Full name: Fashion Culture DataBase

Consists of 76 million geo-tagged images in 16 cosmopolitan cities.

Source: [Changing Fashion Cultures](/paper/changing-fashion-cultures)

Introducing paper: Changing Fashion Cultures
FDDB-360

A 360-degree fisheye-like version of the popular FDDB face detection dataset.

Source: [FDDB-360: Face Detection in 360-degree Fisheye Images](/paper/fddb-360-face-detection-in-360-degree-fisheye)

Introducing paper: FDDB-360: Face Detection in 360-degree Fisheye Images
FDF
Full name: Flickr Diverse Faces

A diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds. 

Source: [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](/paper/deepprivacy-a-generative-adversarial-network)

Introducing paper: DeepPrivacy: A Generative Adversarial Network for Face Anonymization
FDST
Full name: Fudan-ShanghaiTech

The **Fudan-ShanghaiTech** dataset (**FDST**) is a dataset for video crowd counting. It contains 15K frames with about 394K annotated heads captured from 13 different scenes

Source: [https://arxiv.org/abs/1907.07911](https://arxiv.org/abs/1907.07911)

Introducing paper: Locality-constrained Spatial Transformer Network for Video Crowd Counting
FeathersV1

The FeatherV1 dataset is a dataset for fine-grained visual classification. It contains 28,272 images of feathers categorized by 595 bird species.

Source: [https://github.com/feathers-dataset/feathersv1-dataset](https://github.com/feathers-dataset/feathersv1-dataset)
Image Source: [https://github.com/feathers-dataset/feathersv1-dataset](https://github.com/feathers-dataset/feathersv1-dataset)

Introducing paper: Feathers dataset for Fine-Grained Visual Categorization
FewGlue

FewGLUE consists of a random selection of 32 training examples from the SuperGLUE training sets and up to 20,000 unlabeled examples for each SuperGLUE task.

Source: [FewGlue](https://github.com/timoschick/fewglue)

Introducing paper: It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners