Omkar2118/datasets huggingface dataset:
aGender
Full name: aGender

The **aGender** corpus contains audio recordings of predefined utterances and free speech produced by humans of different age and gender. Each utterance is labeled as one of four age groups: Child, Youth, Adult, Senior, and as one of three gender classes: Female, Male and Child.

Source: [Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data](https://arxiv.org/abs/1602.05875)
Image Source: [http://www.lrec-conf.org/proceedings/lrec2010/pdf/262_Paper.pdf](http://www.lrec-conf.org/proceedings/lrec2010/pdf/262_Paper.pdf)

Introducing paper: A Database of Age and Gender Annotated Telephone Speech
DCASE 2014
Full name: DCASE 2014

DCASE2014 is an audio classification benchmark.

Source: [Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization](https://arxiv.org/abs/1807.00230)

Introducing paper: Detection and Classification of Acoustic Scenes and Events
LOCATA
Full name: LOCATA

The **LOCATA** dataset is a dataset for acoustic source localization. It consists of real-world ambisonic speech recordings with optically tracked azimuth-elevation labels.

Source: [Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks](https://arxiv.org/abs/1904.08452)
Image Source: [https://www.locata.lms.tf.fau.de/files/2018/05/LOCATA_Paper_SAM_Workshop_2018.pdf](https://www.locata.lms.tf.fau.de/files/2018/05/LOCATA_Paper_SAM_Workshop_2018.pdf)

Introducing paper: The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking
ISIC 2017 Task 1
Full name: ISIC 2017 Task 1

The ISIC 2017 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 1 challenge dataset for lesion segmentation contains 2,000 images for training with ground truth segmentations (2000 binary mask images).

Source: [https://challenge.isic-archive.com/landing/2017/42](https://challenge.isic-archive.com/landing/2017/42)
Image Source: [https://challenge.isic-archive.com/landing/2017/42](https://challenge.isic-archive.com/landing/2017/42)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
ISIC 2017 Task 2
Full name: ISIC 2017 Task 2

The ISIC 2017 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 2 challenge dataset for lesion dermoscopic feature extraction contains the original lesion image, a corresponding superpixel mask, and superpixel-mapped expert annotations of the presence and absence of the following features: (a) network, (b) negative network, (c) streaks and (d) milia-like cysts.

Source: [https://challenge.isic-archive.com/landing/2017/43](https://challenge.isic-archive.com/landing/2017/43)
Image Source: [https://challenge.isic-archive.com/landing/2017/43](https://challenge.isic-archive.com/landing/2017/43)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
ISIC 2017 Task 3
Full name: ISIC 2017 Task 3

The ISIC 2017 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 3 challenge dataset for lesion classification contains 2,000 images for training including 374 melanoma, 254 seborrheic keratosis and the remainder as benign nevi (1372).

Source: [https://challenge.isic-archive.com/landing/2017/42](https://challenge.isic-archive.com/landing/2017/42)
Image Source: [https://challenge.isic-archive.com/landing/2017/44](https://challenge.isic-archive.com/landing/2017/44)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
ISIC 2018 Task 1
Full name: ISIC 2018 Task 1

The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. This Task 1 dataset is the challenge on lesion segmentation. It includes 2594 images.

Source: [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://arxiv.org/abs/1909.00166)
Image Source: [https://challenge2018.isic-archive.com/task1/](https://challenge2018.isic-archive.com/task1/)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
ISIC 2018 Task 2
Full name: ISIC 2018 Task 2

The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 2 dataset is the challenge on lesion attribute detection. It includes 2594 images. The task is to detect the following dermoscopic attributes: pigment network, negative network, streaks, mila-like cysts and globules (including dots).

Source: [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://arxiv.org/abs/1909.00166)
Image Source: [https://challenge2018.isic-archive.com/task2/](https://challenge2018.isic-archive.com/task2/)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
ISIC 2018 Task 3
Full name: ISIC 2018 Task 3

The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 3 dataset is the challenge on lesion classification. It includes 2594 images. The task is to classify the dermoscopic images into one of the following categories: melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis / Bowen’s disease, benign keratosis, dermatofibroma, and vascular lesion.

Source: [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://arxiv.org/abs/1909.00166)
Image Source: [https://challenge2018.isic-archive.com/task3/](https://challenge2018.isic-archive.com/task3/)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
BCN_20000
Full name: BCN_20000

**BCN_20000** is a dataset composed of 19,424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. The dataset can be used for lesion recognition tasks such as lesion segmentation, lesion detection and lesion classification.

Source: [https://arxiv.org/abs/1908.02288](https://arxiv.org/abs/1908.02288)
Image Source: [https://arxiv.org/abs/1908.02288](https://arxiv.org/abs/1908.02288)

Introducing paper: BCN20000: Dermoscopic Lesions in the Wild
MSK
Full name: MSK

The **MSK** dataset is a dataset for lesion recognition from the Memorial Sloan-Kettering Cancer Center. It is used as part of the ISIC lesion recognition challenges.

Source: [https://arxiv.org/pdf/1710.05006.pdf](https://arxiv.org/pdf/1710.05006.pdf)
Image Source: [https://arxiv.org/pdf/1902.03368.pdf](https://arxiv.org/pdf/1902.03368.pdf)

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
NeuB1
Full name: NeuB1

**NeuB1** is a microscopic neuronal image dataset for retinal vessel segmentation, which contains 112 images of size 512 x 152. The train/test split is 37/75.
Image Source: [https://web.bii.a-star.edu.sg/~zhaoh/Jaydeep_Tracing/](https://web.bii.a-star.edu.sg/~zhaoh/Jaydeep_Tracing/)