Omkar2118/datasets huggingface dataset:
PubMed RCT
Full name: PubMed 200k RCT

**PubMed 200k RCT** is new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: the authors hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.

Source: [GitHub](https://github.com/Franck-Dernoncourt/pubmed-rct)
Image Source: [https://arxiv.org/pdf/1710.06071.pdf](https://arxiv.org/pdf/1710.06071.pdf)

Introducing paper: PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
NSIDES
Full name: Offsides and Twosides (NSIDES v0.1)

Drug side effects and drug-drug interactions were mined from publicly available data. Offsides is a database of drug side-effects that were found, but are not listed on the official FDA label. Twosides is the only comprehensive database drug-drug-effect relationships. Over 3,300 drugs and 63,000 combinations connected to millions of potential adverse reactions.

Source: [http://tatonettilab.org/offsides/](http://tatonettilab.org/offsides/)
Image Source: [http://doi.org/10.1126/scitranslmed.3003377](http://doi.org/10.1126/scitranslmed.3003377)

Introducing paper: Data-driven prediction of drug effects and interactions
MuTual
Full name: MuTual

**MuTual** is a retrieval-based dataset for multi-turn dialogue reasoning, which is modified from Chinese high school English listening comprehension test data. It tests dialogue reasoning via next utterance prediction.

Source: [https://github.com/Nealcly/MuTual](https://github.com/Nealcly/MuTual)
Image Source: [https://github.com/Nealcly/MuTual](https://github.com/Nealcly/MuTual)

Introducing paper: MuTual: A Dataset for Multi-Turn Dialogue Reasoning
CRIM13
Full name: Caltech Resident-Intruder Mouse 13

The Caltech Resident-Intruder Mouse dataset (**CRIM13**) consists of 237x2 videos (recorded with synchronized top and side view) of pairs of mice engaging in social behavior, catalogued into thirteen different actions. Each video lasts ~10min, for a total of 88 hours of video and 8 million frames. A team of behavior experts annotated each video on a frame-by-frame basis for a state-of-the-art study of the neurophysiological mechanisms involved in aggression and courtship in mice.

Source: [https://pdollar.github.io/research.html](https://pdollar.github.io/research.html)
Image Source: [https://authors.library.caltech.edu/104600/1/2020.07.26.222299v1.full.pdf](https://authors.library.caltech.edu/104600/1/2020.07.26.222299v1.full.pdf)
Imagewoof
Full name: Imagewoof

**Imagewoof** is a subset of 10 dog breed classes from Imagenet. The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog.

Source: [https://github.com/fastai/imagenette](https://github.com/fastai/imagenette)
Image Source: [https://medium.com/@lessw/how-we-beat-the-fastai-leaderboard-score-by-19-77-a-cbb2338fab5c](https://medium.com/@lessw/how-we-beat-the-fastai-leaderboard-score-by-19-77-a-cbb2338fab5c)

Introducing paper: fastai: A Layered API for Deep Learning
Imagenette
Full name: Imagenette

**Imagenette** is a subset of 10 easily classified classes from Imagenet (bench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).

Source: [https://github.com/fastai/imagenette](https://github.com/fastai/imagenette)
Image Source: [https://docs.fast.ai/tutorial.imagenette.html](https://docs.fast.ai/tutorial.imagenette.html)

Introducing paper: fastai: A Layered API for Deep Learning
Stanford-ECM
Full name: Stanford-ECM

**Stanford-ECM** is an egocentric multimodal dataset which comprises about 27 hours of egocentric video augmented with heart rate and acceleration data. The lengths of the individual videos cover a diverse range from 3 minutes to about 51 minutes in length. A mobile phone was used to collect egocentric video at 720x1280 resolution and 30 fps, as well as triaxial acceleration at 30Hz. The mobile phone was equipped with a wide-angle lens, so that the horizontal field of view was enlarged from 45 degrees to about 64 degrees. A wrist-worn heart rate sensor was used to capture the heart rate every 5 seconds. The phone and heart rate monitor was time-synchronized through Bluetooth, and all data was stored in the phone’s storage. Piecewise cubic polynomial interpolation was used to fill in any gaps in heart rate data. Finally, data was aligned to the millisecond level at 30 Hz.

Source: [http://ai.stanford.edu/~syyeung/ecm_dataset/egocentric_multimodal.html](http://ai.stanford.edu/~syyeung/ecm_dataset/egocentric_multimodal.html)
Image Source: [http://ai.stanford.edu/~syyeung/ecm_dataset/egocentric_multimodal.html](http://ai.stanford.edu/~syyeung/ecm_dataset/egocentric_multimodal.html)

Introducing paper: Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals
MSRA Hand
Full name: MSRA Hand

**MSRA Hand**s is a dataset for hand tracking. In total 6 subjects' right hands are captured using Intel's Creative Interactive Gesture Camera. Each subject is asked to make various rapid gestures in a 400-frame video sequence. To account for different hand sizes, a global hand model scale is specified for each subject: 1.1, 1.0, 0.9, 0.95, 1.1, 1.0 for subject 1~6, respectively.
The camera intrinsic parameters are: principle point = image center(160, 120), focal length = 241.42. The depth image is 320x240, each *.bin file stores the depth pixel values in row scanning order, which are 320*240 floats. The unit is millimeters. The bin file is binary and needs to be opened with std::ios::binary flag.
joint.txt file stores 400 frames x 21 hand joints per frame. Each line has 3 * 21 = 63 floats for 21 3D points in (x, y, z) coordinates. The 21 hand joints are: wrist, index_mcp, index_pip, index_dip, index_tip, middle_mcp, middle_pip, middle_dip, middle_tip, ring_mcp, ring_pip, ring_dip, ring_tip, little_mcp, little_pip, little_dip, little_tip, thumb_mcp, thumb_pip, thumb_dip, thumb_tip.
The corresponding *.jpg file is just for visualization of depth and ground truth joints.

Source: [https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt](https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt)
Image Source: [https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf)

Introducing paper: Realtime and Robust Hand Tracking from Depth
MSRA10K
Full name: MSRA10K Salient Object Database

**MSRA10K** is a dataset for salient object detection that contains 10,000 images with pixel-level saliency labeling for 10K images from the MSRA salient object detection dataset. The original MRSA database provides salient object annotation in terms of bounding boxes provided by 3-9 users.

Source: [https://mmcheng.net/msra10k/](https://mmcheng.net/msra10k/)
Image Source: [https://mmcheng.net/msra10k/](https://mmcheng.net/msra10k/)
AwA2
Full name: Animals with Attributes 2

**Animals with Attributes 2** (**AwA2**) is a dataset for benchmarking transfer-learning algorithms, such as attribute base classification and zero-shot learning. AwA2 is a drop-in replacement of original Animals with Attributes (AwA) dataset, with more images released for each category. Specifically, AwA2 consists of in total 37322 images distributed in 50 animal categories. The AwA2 also provides a category-attribute matrix, which contains an 85-dim attribute vector (e.g., color, stripe, furry, size, and habitat) for each category.

Source: [Learning from Noisy Web Data with Category-level Supervision](https://arxiv.org/abs/1803.03857)
Image Source: [https://arxiv.org/pdf/1604.00326.pdf](https://arxiv.org/pdf/1604.00326.pdf)

Introducing paper: Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly