Omkar2118/datasets huggingface dataset:
DyML-Vehicle
Full name: Dynamic Metric Learning Vehicle

DyML-Vehicle merges two vehicle re-ID datasets PKU VehicleID [1], VERI-Wild [1]. Since these two datasets have only annotations on the identity (fine) level, we manually annotate each image with “model” label (e.g., Toyota Camry, Honda Accord, Audi A4) and “body type” label (e.g., car, suv, microbus, pickup). Moreover, we label all the taxi images as a novel testing class under coarse level.

[1] Hongye Liu, Yonghong Tian, Yaowei Wang, Lu Pang, and Tiejun Huang. Deep relative distance learning: Tell the difference between similar vehicles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2167–2175, 2016. 4

[2] Y. Lou, Y. Bai, J. Liu, S. Wang, and L. Duan. Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3230–3238, 2019. 4

Introducing paper: Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
IDK-MRC

**IDK-MRC** is an Indonesian Machine Reading Comprehension (MRC) dataset  consists of more than 10K questions in total with over 5K unanswerable questions with diverse question types.

Source: [IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
](https://arxiv.org/pdf/2210.13778v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.13778v1.pdf](https://arxiv.org/pdf/2210.13778v1.pdf)

Introducing paper: IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
DyML-Animal
Full name: Dynamic Metric Learning Animal

DyML-Animal is based on animal images selected from ImageNet-5K [1]. It has 5 semantic scales (i.e., classes, order, family, genus, species) according to biological taxonomy. Specifically, there are 611 “species” for the fine level, 47 categories corresponding to “order”, “family” or “genus” for the middle level, and 5 “classes” for the coarse level. We note some animals have contradiction between visual perception and biological taxonomy, e.g., whale in “mammal” actually looks more similar to fish. Annotating the whale images as belonging to mammal would cause confusion to visual recognition. So we take a detailed check on potential contradictions and intentionally leave out those animals.

[1] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 5

Introducing paper: Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
DyML-Product
Full name: Dynamic Metric Learning Product

DyML-Product is derived from iMaterialist-2019, a hierarchical online product dataset. The original iMaterialist-2019 offers up to 4 levels of hierarchical annotations. We remove the coarsest level and maintain 3 levels for DyML-Product.

https://github.com/MalongTech/imaterialist-product-2019

Introducing paper: Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
CCSE
Full name: Chinese Character Stroke Extraction

**Chinese Character Stroke Extraction (CCSE)** is a benchmark containing two large-scale datasets: Kaiti CCSE (CCSE-Kai) and Handwritten CCSE (CCSE-HW). It is designed for stroke extraction problems.

Source: [Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks](https://arxiv.org/pdf/2210.13826v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.13826v1.pdf](https://arxiv.org/pdf/2210.13826v1.pdf)

Introducing paper: Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Kor-Learner
Full name: Korean Learner Corpus

**Kor-Learner** is a Korean grammatical error correction (GEC) dataset made from the NIKL learner corpus containing essays written by Korean learners and their grammatical error correction annotations by their tutors in an morpheme-level XML file format. It contains more than 28K sentence pairs.

Source: [Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation](https://arxiv.org/pdf/2210.14389v2.pdf)

Image Source: [https://arxiv.org/pdf/2210.14389v2.pdf](https://arxiv.org/pdf/2210.14389v2.pdf)

Introducing paper: Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
Kor-Native
Full name: Native Korean Corpus

**Kor-Learner** is a Korean grammatical error correction (GEC) dataset collected grammatically from two sources, and the correct sentences were read using Google Text-to-Speech(TTS) system. The general public was tasked with dictating grammatically correct sentences and transcribe them. It contains more than 17K sentence pairs.

Source: [Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation](https://arxiv.org/pdf/2210.14389v2.pdf)

Image Source: [https://arxiv.org/pdf/2210.14389v2.pdf](https://arxiv.org/pdf/2210.14389v2.pdf)

Introducing paper: Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
Kor-Lang8
Full name: Lang-8 Korean Corpus

**Kor-Lang8** is a Korean grammatical error correction (GEC) dataset extracted from the NAIST Lang-8 Learner Corpora by the language label. It contains more than 109K sentence pairs.

Source: [Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation](https://arxiv.org/pdf/2210.14389v2.pdf)

Image Source: [https://arxiv.org/pdf/2210.14389v2.pdf](https://arxiv.org/pdf/2210.14389v2.pdf)

Introducing paper: Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
pmuBAGE

**pmuBAGE** (the Benchmarking Assortment of Generated PMU Events) is a dataset that consists of almost 1000 instances of labeled event data to encourage benchmark evaluations on phasor measurement unit (PMU) data analytics. PMU data are challenging to obtain, especially those covering event periods. Nevertheless, power system problems have recently seen phenomenal advancements via data-driven machine learning solutions. A highly accessible standard benchmarking dataset would enable a drastic acceleration of the development of successful machine learning techniques in this field.

Source: [](https://arxiv.org/pdf/2210.14204v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.14204v1.pdf](https://arxiv.org/pdf/2210.14204v1.pdf)

Introducing paper: pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events