Omkar2118/datasets huggingface dataset:
Ultra-processed Food Dataset

The raw data are obtained from an industrial plant for ultra-processed food production. The sampling was carried
out every 5 minutes while the total production cycle takes
approximately 3 hours, from raw ingredients to final semi-
finished products. The extracted data represent approximately
80 days of production.  Variables 2 − 14 belonging to 4 specific phases of the process and
influence the qualitative variable 17. Variables 15 and 16 are
external variables not controlled by the process which affect
the final product. It should also be noted that some variation
may be due to changes in raw materials, in production flow
(variable 1) or to possible reconfiguration between weeks.
However while the magnitude of effects may change between
weeks, the causal relationships are dictated by the plant and
process dynamics and are consistent (at the best of potential
un-cofounder and faults) throughout the production .

Introducing paper: CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
Auditory Detection of Sound (ADS)

Test dataset for unsupervised anomaly detection in sound (ADS).
Sequence Consistency Evaluation (SCE) tests

Sequence Consistency Evaluation (SCE) consists of a benchmark task for sequence consistency evaluation (SCE).

Introducing paper: Naive Few-Shot Learning: Uncovering the fluid intelligence of machines
Bone Age
Full name: The RSNA Pediatric Bone Age Machine Learning Challenge

At RSNA 2017 there was a contest to correctly identify the age of a child from an X-ray of their hand.
MPHOI-72
Full name: Multi-person Human-object Interaction Dataset 72

MPHOI-72 is a multi-person human-object interaction dataset that can be used for a wide variety of HOI/activity recognition and pose estimation/object tracking tasks. The dataset is challenging due to many body occlusions among the humans and objects. It consists of 72 videos captured from 3 different angles at 30 fps, with totally 26,383 frames and an average length of 12 seconds. It involves 5 humans performing in pairs, 6 object types, 3 activities and 13 sub-activities. The dataset includes color video, depth video, human skeletons, human and object bounding boxes.

Introducing paper: Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
NeRF
Full name: Neural Radiance Fields

Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels.

Introducing paper: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
LLFF
Full name: Local Light Field Fusion

Local Light Field Fusion (LLFF) is a practical and robust deep learning solution for capturing and rendering novel views of complex real-world scenes for virtual exploration. The dataset consists of both renderings and real images of natural scenes. The synthetic images are rendered from the SUNCG and UnrealCV where SUNCG contains 45000 simplistic house and room environments with texture-mapped surfaces and low geometric complexity. UnrealCV contains a few large-scale environments modeled and rendered with extreme detail. The real images are 24 scenes captured from a handheld cellphone.

Introducing paper: Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Mip-NeRF 360
Full name: Unbounded Anti-Aliased Neural Radiance Fields

Mip-NeRF 360 is an extension to the Mip-NeRF that uses a non-linear parameterization, online distillation, and a novel distortion-based regularize to overcome the challenge of unbounded scenes. The dataset consists of 9 scenes with 5 outdoors and 4 indoors, each containing a complex central object or area with a detailed background.

Introducing paper: Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
VLN-CE
Full name: Vision-and-Language Navigation in Continuous Environments

Vision and Language Navigation in Continuous Environments (VLN-CE) is an instruction-guided navigation task with crowdsourced instructions, realistic environments, and unconstrained agent navigation. The dataset consists of 4475 trajectories converted from Room-to-Room train and validation splits. For each trajectory, multiple natural language instructions from Room-to-Room and a pre-computed shortest path are provided following the waypoints via low-level actions.

Introducing paper: Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments
ImageCoDe
Full name: Image Retrieval from Contextual Descriptions

Given 10 minimally contrastive (highly similar) images and a complex description for one of them, the task is to retrieve the correct image.
The source of most images are videos and descriptions as well as retrievals come from human.

Introducing paper: Image Retrieval from Contextual Descriptions
WHU Building Dataset

We manually edited an aerial and a satellite imagery dataset of building samples and named it a WHU building dataset. The aerial dataset consists of more than 220, 000 independent buildings extracted from aerial images with 0.075 m spatial resolution and 450 km2 covering in Christchurch, New Zealand. The satellite imagery dataset consists of two subsets. One of them is collected from cities over the world and from various remote sensing resources including QuickBird, Worldview series, IKONOS, ZY-3, etc. The other satellite building sub-dataset consists of 6 neighboring satellite images covering 550 km2 on East Asia with 2.7 m ground resolution.
Handwritten Devanagari Character Recognition

This is an image database of Handwritten Devanagari characters. There are 46 classes of characters with 2000 examples each. The dataset is split into training set(85%) and testing set(15%).


Citation: S. Acharya, A.K. Pant and P.K. Gyawali Deep Learning Based Large Scale Handwritten Devanagari Character Recognition ,In Proceedings of the 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 121-126, 2015.
H2O  (2 Hands and Objects)

We present a comprehensive framework for egocentric interaction recognition using markerless 3D annotations of two hands manipulating objects. To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition. Our method produces annotations of the 3D pose of two hands and the 6D pose of the manipulated objects, along with their interaction labels for each frame. Our dataset, called H2O (2 Hands and Objects), provides synchronized multi-view RGB-D images, interaction labels, object classes, ground-truth 3D poses for left & right hands, 6D object poses, ground-truth camera poses, object meshes and scene point clouds. To the best of our knowledge, this is the first benchmark that enables the study of first-person actions with the use of the pose of both left and right hands manipulating objects and presents an unprecedented level of detail for egocentric 3D interaction recognition. We further propose the method to predict interaction classes by estimating the 3D pose of two hands and the 6D pose of the manipulated objects, jointly from RGB images. Our method models both inter- and intra-dependencies between both hands and objects by learning the topology of a graph convolutional network that predicts interactions. We show that our method facilitated by this dataset establishes a strong baseline for joint hand-object pose estimation and achieves state-of-the-art accuracy for first person interaction recognition.
FullTextPeerRead

FullTextPeerRead is a dataset created by Jeong et al. for context-aware citation recommendation. It contains context sentences to cited references and paper metadata, which makes it a well-organized dataset for a context-aware paper recommendation.

From paper: A context-aware citation recommendation model with BERT and graph convolutional networks
arXiv-200

A newly proposed dataset for local citation recommendation, consisting of 3.2 million local citation sentences along
with the title and the abstract of both the citing and the cited papers.  Around 1.66 million papers' titles and abstracts are available in the database.

Introducing paper: Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking