Omkar2118/datasets huggingface dataset:
LPR4M
Full name: Livestreaming Product Recognition 4M

**LPR4M** is a large-scale live commerce dataset, offering a significantly broader coverage of categories and diverse modalities such as video, image, and text. It contains 4M exactly matched〈clip, image〉pairs of 4M live clips, and 332k shop images. Each image has 12 clips with different product variations, e.g., viewpoint, scale, and occlusion.

Introducing paper: Cross-view Semantic Alignment for Livestreaming Product Recognition
IDiff-Face

The IDiff-Face dataset was proposed in the paper "IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models". This dataset is synthetically generated using the IDiff-Face model.

Introducing paper: IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
GVLM
Full name: Global Very-High-Resolution Landslide Mapping

For change detection tasks, current open-source datasets mainly focus on building extraction (e.g., WHU building dataset and LEVIR-CD dataset) (Chen and Shi, 2020; Ji et al., 2018) and urban development monitoring (e.g., SECOND dataset, Google dataset and CDD dataset) (Yang et al., 2022; Peng et al., 2021; Lebedev et al., 2018), whereas datasets for natural disaster monitoring have been seldom investigated. 

Therefore, we sought to present the GVLM dataset, the first large-scale and open-source VHR landslide mapping dataset. It includes $17$ bitemporal very-high-resolution imagery pairs with a spatial resolution of $0.59$ m acquired via Google Earth service. Each sub-dataset contains a pair of bitemporal images and the corresponding ground-truth map. The total coverage of the dataset is $163.77 km2$. The landslide sites in different geographical locations have various sizes, shapes, occurrence times, spatial distributions, phenology states, and land cover types, resulting in considerable spectral heterogeneity and intensity variations in the remote sensing imagery. The GVLM dataset can be used to develop and evaluate machine/deep learning models for change detection, semantic segmentation and landslide extraction.
Vessel detection Dateset

a vessel dataset using 85 videos. The dataset covers normal weather conditions such as sunny, rainy, reflective, low light, night, etc. The first 43 videos are annotated with one video every 1 second, and the last 42 videos are annotated with one video every 2 seconds. Furthermore, since the collected videos contain many duplicate images, we use multiple similar images to annotate only one image to alleviate overfitting when training the model, with a total of 4563 images and 5864 annotation frames. Among them, 82 videos are used as the training model, of which 3063 are used as the training set, 1318 as the validation set, and three untrained scenes are used as the test set, totaling 182 images.

Introducing paper: VDDT: Improving Vessel Detection with Deformable Transfomer
CG-Eval
Full name: CG-Eval

This paper presents CG-Eval, the first comprehensive evaluation of the generation capabilities of large Chinese language models across a wide range of academic disciplines. The models' performance was assessed based on their ability to generate accurate and relevant responses to different types of questions in six disciplines, namely, Science and Engineering, Humanities and Social Sciences, Mathematical Calculations, Medical Practitioner Qualification Examination, Judicial Examination, and Certified Public Accountant Examination. This paper also presents Gscore, a composite index derived from the weighted sum of multiple metrics to measure the quality of model's generation against a reference.

Introducing paper: Evaluating the Generation Capabilities of Large Chinese Language Models
Inshorts News
Full name: Inshorts English News dataset

Inshorts News dataset Inshorts provides a news summary in 60 words or less. Inshorts is a news service that offers short summaries of news from around the web. This dataset contains headlines and a summary of news items and their source.

Introducing paper: An abstractive text summarization technique using transformer model with self-attention mechanism
BabbleCor

What is BabbleCor?

BabbleCor is a crosslinguistic corpus of infant and child vocalizations from 52 children exposed to five different languages: English, Spanish, Tsimane', Yêlí-Dnye, Tseltal Mayan, and bilingual Quechua-Spanish.

How was BabbleCor created?

BabbleCor consists of very short audio clips (approximately 400ms) of child vocalizations. To generate these clips, each child first completed a daylong audio recording, between 6 and 16 hours in length, where a small, lightweight recorder was worn inside of a clothing pocket designed for the device.

From these daylong recordings, child vocalizations were either identified by the proprietary Language ENvironment Analysis algorithm, which assigns utterances to speakers in naturalistic audio recordings (e.g. Female Adult, Child) or the vocalizations were identified by hand. 100 of the utterances identified as child vocalizations were randomly selected and chopped into the smaller clips in BabbleCor.

Where do the BabbleCor clip annotations come from?

Each short clip (~400ms) was categorized according to a 5-way scheme by citizen science annotators on the iHEARu PLAY platform (https://www.ihearu-play.eu/). Annotators classified clips as 1) canonical - containing a consonant to vowel transition, 2) non-canonical - not containing a consonant to vowel transition, 3) crying, 4) laughing, or 5) junk.

For further details on corpus creation, please see Methods described in Cychosz et al. (2021).

What are the metadata?

There are two metadata components in BabbleCor: Annotation_Tags and Public_Metadata. As the name suggests, Public_Metadata includes corpus metadata that is publicly available to all corpus users: child ID, child age, child's assigned gender, corpus of origin, and clip ID. Annotation_Tags contains the annotation tags for each clip ID, such as canonical babble, laughing, etc. For access to the annotation tags, please sign, scan, & email the data sharing agreement to babblecorpus@gmail.com (see Data_Sharing_Agreement).
Nikon RAW Low Light
Full name: Nikon Camera Low Light RAW Image Dataset

Dataset release for the BMVC 2021 Paper "Few-Shot Domain Adaptation for Low Light RAW Image Enhancement"

Abstract: Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain’s enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images. The code is available at https://val.cds.iisc.ac.in/HDR/BMVC21/index.html.

Introducing paper: Few-Shot Domain Adaptation for Low Light RAW Image Enhancement
Canon RAW Low Light
Full name: Canon Camera Low Light RAW Image Dataset

The goal of this project is to present two new datasets that seek to expand the capability of the Learning to See in the Dark Low-light enhancement CNN for the Canon 6D DSLR, and explore how the network performs when modified in various ways, both pruning it and making it deeper.

The original paper Learning to See in the Dark was published in CVPR 2018, by Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun.