Omkar2118/datasets huggingface dataset:
Matbench

The Matbench test suite v0.1 contains 13 supervised ML tasks from 10 datasets. Matbench’s data are sourced from various subdisciplines of materials science, such as experimental mechanical properties (alloy strength), computed elastic properties, computed and experimental electronic properties, optical and phonon properties, and thermodynamic stabilities for crystals, 2D materials, and disordered metals. The number of samples in each task ranges from 312 to 132,752, representing both relatively scarce experimental materials properties and comparatively abundant properties such as DFT-GGA formation energies. Each task is a self-contained dataset containing a single material primitive as input (either composition or composition plus crystal structure) and target property as output for each sample.
SKINL2
Full name: Light Field Image Dataset of Skin Lesions

The SKINL2 dataset comprises a total of 376 light fields acquired under similar conditions. The images were classified using eight categories, according to the type of skin lesion/ICD code: 

    Melanoma / C43
    Melanocytic Nevus / D22
    Basal Cell Carcinoma / D04
    Seborrheic Keratosis / L82
    Hemangioma / D18
    Dermatofibroma / D23s
    Psoriasis / L40
    Other

S. M. M. de Faria et al., "Light Field Image Dataset of Skin Lesions," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 3905-3908. DOI: 10.1109/EMBC.2019.8856578
SD-198

The SD-198 dataset contains 198 different diseases from different types of eczema, acne and various cancerous conditions. There are 6,584 images in total. A subset include the classes with more than 20 image samples, namely SD-128."
MED-NODE
Full name: Dermatology database used in MED-NODE

"Our dataset consists of 70 melanoma and 100 naevus images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen (UMCG) used for the development and testing of the MED-NODE system for skin cancer detection from macroscopic images. The file - complete_mednode_dataset.zip 24KB - contains 170 images (70 melanoma and 100 nevi cases)."

I. Giotis, N. Molders, S. Land, M. Biehl, M.F. Jonkman and N. Petkov: "MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images", Expert Systems with Applications, 42 (2015), 6578-6585
7-point criteria evaluation Database

"We provide a database for evaluating computerized image-based prediction of the 7-point skin lesion malignancy checklist. The dataset includes over 2000 clinical and dermoscopy color images, along with corresponding structured metadata tailored for training and evaluating computer aided diagnosis (CAD) systems. "
NVIDIA Synthetic Head Dataset

This dataset contains 500K high photo-real rendered images of 10 real head models with (yaw, pitch, roll) head pose labels. Each head is rendered with a different pose and environmental lighting.

Introducing paper: Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network
MSU Video Upscalers: Quality Enhancement

The dataset aims to find the algorithms that produce the most visually pleasant image possible and generalize well to a broad range of content. It consists of 30 clips and contains 15 2D-animated segments losslessly recorded from various video games and 15 camera-shot segments from high-bitrate YUV444 sources. The complexity of clips varies significantly in terms of spatial and temporal indexes. Multiple bicubic downscaling mixed with sharpening is used to simulate complex real-world camera degradation. The authors used slight compression and YUV420 conversion to simulate a practical use case. 1920×1080 sources were downscaled to 480×270 input.
UMAD
Full name: Urban Minimum Altitude Dataset

The UMAD is a virtual-scene dataset made by AirSim, which is a simulator built on Unreal Engine. In order to ensure the simulation data is as close to real as possible, on the one hand, we use a realistic city scene model which comes from Kirill Sibiriakov, on the other hand, we collect the vehicle motion data and camera data separately to enable the frequency and quality, which is derived from the method in the paper. In comparison to urban datasets which are created by using the oblique aerial photography technique, our dataset has higher fidelity when it comes to the texture details

Introducing paper: LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
CodeQueries

CodeQueries Benchmark dataset consists of instances of semantic queries, code context and code spans in the context corresponding to the semantic queries. The dataset can be used in experiments involving semantic query comprehension with an extractive question-answering methodology over code. More details can be found in the [paper](https://arxiv.org/abs/2209.08372).

Introducing paper: CodeQueries: A Dataset of Semantic Queries over Code