Omkar2118/datasets huggingface dataset:
pic2kcal

The pic2kal benchmark for calorie prediction contains 308,000 images from over 70,000 recipes including photographs, ingredients and instructions, matched with nutritional information.

Source: [https://arxiv.org/abs/2011.01082](https://arxiv.org/abs/2011.01082)
Image Source: [https://github.com/phiresky/pic2kcal](https://github.com/phiresky/pic2kcal)

Introducing paper: Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information
PicTropes

PicTropes is a dataset of films and the tropes that they use created from the database DBTropes.org.

Source: [Overview of PicTropes, a film trope dataset](/paper/overview-of-pictropes-a-film-trope-dataset)

Introducing paper: Overview of PicTropes, a film trope dataset
Pinterest Complete The Look

The Pinterest Complete the Look dataset consists of over 1 million outfits and 4 million objects. It can be used to predict style compatibility between fashion items in order to recommend complementary items that complete an outfit.

Source: [https://arxiv.org/abs/2006.10792](https://arxiv.org/abs/2006.10792)
Image Source: [https://github.com/eileenforwhat/complete-the-look-dataset](https://github.com/eileenforwhat/complete-the-look-dataset)

Introducing paper: Bootstrapping Complete The Look at Pinterest
pioNER

The **pioNER** corpus provides gold-standard and automatically generated named-entity datasets for the Armenian language.
The automatically generated corpus is generated from Wikipedia. The gold-standard set is a collection of over 250 news articles from iLur.am with manual named-entity annotation. It includes sentences from political, sports, local and world news, and is comparable in size with the test sets of other languages.

Source: [https://github.com/ispras-texterra/pioner](https://github.com/ispras-texterra/pioner)

Introducing paper: pioNER: Datasets and Baselines for Armenian Named Entity Recognition
Plaintext Jokes

There are about 208 000 jokes in this database scraped from three sources.

Source: [Plaintext Jokes](https://github.com/taivop/joke-dataset)
Planar Manipulator Dataset

The dataset consists of 90 000 color videos that show a planar robot manipulator executing articulated manipulation tasks. More precisely, the manipulator grasps a circular object of random color and size and places it on top of a square object/platform of again random color and size. The initial conﬁgurations (location, size and color) of the objects were randomly sampled during generation. Different from other datasets such as the moving MNIST dataset, the samples comprise a goal-oriented task as described, making it more suitable for testing prediction capabilities of an ML model. For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.

Source: [https://github.com/ferreirafabio/PlanarManipulatorDataset](https://github.com/ferreirafabio/PlanarManipulatorDataset)
Image Source: [https://github.com/ferreirafabio/PlanarManipulatorDataset](https://github.com/ferreirafabio/PlanarManipulatorDataset)

Introducing paper: Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
PlantDoc

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images.

Source: [PlantDoc: A Dataset for Visual Plant Disease Detection](/paper/plantdoc-a-dataset-for-visual-plant-disease)

Introducing paper: PlantDoc: A Dataset for Visual Plant Disease Detection
Plant Seedlings Dataset

A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per mm.

Source: [A Public Image Database for Benchmark of Plant Seedling Classification Algorithms](/paper/a-public-image-database-for-benchmark-of)

Introducing paper: A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
PMC-SA
Full name: PMC Structured Abstracts

**PMC-SA** (**PMC Structured Abstracts**) is a dataset of academic publications, used for the task of structured summarization.

Source: [https://arxiv.org/abs/1905.07695](https://arxiv.org/abs/1905.07695)

Introducing paper: Structured Summarization of Academic Publications
PMIndia

Consists of parallel sentences which pair 13 major languages of India with English. The corpus includes up to 56000 sentences for each language pair.

Source: [PMIndia -- A Collection of Parallel Corpora of Languages of India](/paper/pmindia-a-collection-of-parallel-corpora-of)

Introducing paper: PMIndia -- A Collection of Parallel Corpora of Languages of India
pn-summary

Pn-summary is a dataset for Persian abstractive text summarization.

Source: [https://arxiv.org/abs/2012.11204](https://arxiv.org/abs/2012.11204)
Image Source: [https://github.com/hooshvare/pn-summary](https://github.com/hooshvare/pn-summary)

Introducing paper: Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
PoC
Full name: Points of correspondence

A dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. The dataset bridges the gap between coreference resolution and summarization.

Source: [Understanding Points of Correspondence between Sentences for Abstractive Summarization](/paper/understanding-points-of-correspondence)

Introducing paper: Understanding Points of Correspondence between Sentences for Abstractive Summarization
PointDenoisingBenchmark

The **PointDenoisingBenchmark** dataset features 28 different shapes, split into 18 training shapes and 10 test shapes.

* PointDenoisingBenchmark for outliers removal: contains noisy point clouds with different levels of gaussian noise and the corresponding clean ground truths.
* PointDenoisingBenchmark for denoising: contains noisy point clouds with different levels of noise and density of outliers and the corresponding clean ground truths.

Source: [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](/paper/pointcleannet-learning-to-denoise-and-remove)

Introducing paper: PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
PoKi

**PoKi** is a corpus of 61,330 poems written by children from grades 1 to 12. PoKi is especially useful in studying child language because it comes with information about the age of the child authors (their grade).

Source: [https://github.com/whipson/PoKi-Poems-by-Kids](https://github.com/whipson/PoKi-Poems-by-Kids)

Introducing paper: PoKi: A Large Dataset of Poems by Children
PolEmo 2.0

The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains.

Introducing paper: Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews
PolicyQA

A dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices.

Source: [PolicyQA: A Reading Comprehension Dataset for Privacy Policies](/paper/policyqa-a-reading-comprehension-dataset-for)

Introducing paper: PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Polish Political Advertising Dataset

A dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law.

Source: [Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections](/paper/political-advertising-dataset-the-use-case-of)

Introducing paper: Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections
PolitiFact

Fact-checking (FC) articles which contains pairs (multimodal tweet and a FC-article) from politifact.com.

Source: [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](/paper/where-are-the-facts-searching-for-fact)

Introducing paper: Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
PolSF

Collects five open polarimetric SAR images, which are images of the San Francisco area. These five images come from different satellites at different times, which has great scientific research value. 

Source: [PolSF: PolSAR image dataset on San Francisco](/paper/polsf-polsar-image-dataset-on-san-francisco)

Introducing paper: PolSF: PolSAR image dataset on San Francisco