Omkar2118/datasets huggingface dataset:
FFHQ-Aging

**FFHQ-Aging** is a Dataset of human faces designed for benchmarking age transformation algorithms as well as many other possible vision tasks.
This dataset is an extention of the NVIDIA FFHQ dataset, on top of the 70,000 original FFHQ images, it also contains the following information for each image:
* Gender information (male/female with confidence score)
* Age group information (10 classes with confidence score)
* Head pose (pitch, roll & yaw)
* Glasses type (none, normal or dark)
* Eye occlusion score (0-100, different score for each eye)
* Full semantic map (19 classes, based on CelebAMask-HQ labels)

Source: [https://github.com/royorel/FFHQ-Aging-Dataset](https://github.com/royorel/FFHQ-Aging-Dataset)
Image Source: [https://github.com/royorel/FFHQ-Aging-Dataset](https://github.com/royorel/FFHQ-Aging-Dataset)

Introducing paper: Lifespan Age Transformation Synthesis
FGADR

This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists with intra-rater consistency. The proposed dataset will enable extensive studies on DR diagnosis.

Source: [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](/paper/a-benchmark-for-studying-diabetic-retinopathy)

Introducing paper: A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
FIGRIM
Full name: FIne-GRained Image Memorability

This is a dataset of 9428 images, 1754 of which are target images with memorability scores.
The images span 21 scene categories from the SUN database. Each scene category was chosen to contain at least 300 images of size 700x700 or greater. All images were cropped to 700x700 pixels.

Source: [FIGRIM](http://figrim.mit.edu/)
FinChat
Full name: Finnish Chat Conversations on Everyday Topics

Finnish chat conversation corpus and includes unscripted conversations on seven topics from people of different ages. 

Source: [FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics](/paper/finchat-corpus-and-evaluation-setup-for)

Introducing paper: FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
Fine-grained 3D Pose

A new large-scale dataset that consists of 409 fine-grained categories and 31,881 images with accurate 3D pose annotation.

Source: [Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories](/paper/improving-annotation-for-3d-pose-dataset-of)

Introducing paper: Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories
Fine-Grained R2R

This dataset enriches the benchmark Room-to-Room (R2R) dataset by dividing the instructions into sub-instructions and pairing each of those with their corresponding viewpoints in the path. The overall instruction and trajectory of each sample remains the same.

Source: [https://github.com/YicongHong/Fine-Grained-R2R](https://github.com/YicongHong/Fine-Grained-R2R)

Introducing paper: Sub-Instruction Aware Vision-and-Language Navigation
Finer
Full name: Finnish News Corpus for Named Entity Recognition

Finnish News Corpus for Named Entity Recognition (Finer) is a corpus that consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event,and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source.

Introducing paper: A Finnish News Corpus for Named Entity Recognition
FinnSentiment

FinnSentiment introduces a 27,000 sentence dataset (in Finnish) annotated independently with sentiment polarity by three native annotators. 

Source: [FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity Annotation](/paper/finnsentiment-a-finnish-social-media-corpus)

Introducing paper: FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity Annotation
FIVR-200K

The FIVR-200K dataset has been collected to simulate the problem of Fine-grained Incident Video Retrieval (FIVR). The dataset comprises 225,960 videos associated with 4,687 Wikipedia events and 100 selected video queries.

Introducing paper: FIVR: Fine-grained Incident Video Retrieval
FIW-MM
Full name: Families In Wild Multimedia

A large-scale dataset for recognizing kinship in multimedia which extend FIW with multimedia data (i.e., video, audio, and contextual transcripts). 

Source: [Families In Wild Multimedia (FIW-MM): A Multi-Modal Database for Recognizing Kinship](/paper/families-in-wild-multimedia-fiw-mm-a-multi)

Introducing paper: Families In Wild Multimedia: A Multimodal Database for Recognizing Kinship
FLAME
Full name: Fire Luminosity Airborne-based Machine learning Evaluation

FLAME is a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms.

Source: [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](/paper/aerial-imagery-pile-burn-detection-using-deep)

Introducing paper: Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset
Flickr1024

Contains 1024 pairs of high-quality images and covers diverse scenarios.

Source: [Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution](/paper/flickr1024-a-dataset-for-stereo-image-super)

Introducing paper: Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
Flickr Cropping Dataset

The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches.

Source: [https://arxiv.org/abs/1701.01480](https://arxiv.org/abs/1701.01480)

Introducing paper: Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study
Flightmare Simulator

Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc.

Source: [Flightmare Simulator](https://uzh-rpg.github.io/flightmare/)

Introducing paper: Flightmare: A Flexible Quadrotor Simulator