Omkar2118/datasets huggingface dataset:
PyTorrent
Full name: PyTorrent

PyTorrent  contains 218,814 Python package libraries from PyPI and Anaconda environment. This is because earlier studies have shown that much of the code is redundant and Python packages from these environments are better in quality and are well-documented. PyTorrent enables users (such as data scientists, students, etc.) to build off the shelf machine learning models directly without spending months of effort on large infrastructure.

Introducing paper: PyTorrent: A Python Library Corpus for Large-scale Language Models
Multimodal Emoji Prediction

The twitter emoji dataset obtained from CodaLab comprises of 50 thousand tweets along with the associated emoji label. Each tweet in the dataset has a corresponding numerical label which maps to a specific emoji. The emojis are of the 20 most frequent emojis and hence the labels range from 0 to 19

Introducing paper: Are Emojis Predictable?
DeepGlobe

We observe that satellite imagery is a powerful source of information as it contains more structured and uniform data, compared to traditional images. Although computer vision community has been accomplishing hard tasks on everyday image datasets using deep learning, satellite images are only recently gaining attention for maps and population analysis. This workshop aims at bringing together a diverse set of researchers to advance the state-of-the-art in satellite image analysis.

To direct more attention to such approaches, we propose DeepGlobe Satellite Image Understanding Challenge, structured around three different satellite image understanding tasks. The datasets created and released for this competition may serve as reference benchmarks for future research in satellite image analysis. Furthermore, since the challenge tasks will involve "in the wild" forms of classic computer vision problems, these datasets have the potential to become valuable testbeds for the design of robust vision algorithms, beyond the area of remote sensing.

Introducing paper: DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Massachusetts Roads Dataset
Full name: Road and Building Detection Datasets - Massachusetts Roads Dataset

The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details. If you use any of these datasets for research purposes you should use the following citation in any resulting publications:

```
@phdthesis{MnihThesis,
    author = {Volodymyr Mnih},
    title = {Machine Learning for Aerial Image Labeling},
    school = {University of Toronto},
    year = {2013}
}
```
FMFCC-A

FMFCC-A is a large publicly-available Mandarin dataset for synthetic speech detection, which contains 40,000 synthesized Mandarin utterances that generated by 11 Mandarin TTS systems and two Mandarin VC systems, and 10,000 genuine Mandarin utterance collected from 58 speakers. The FMFCCA dataset is divided into the training, development and evaluation sets, which are used for the research of detection of synthesised Mandarin speech under various previously unknown speech synthesis systems or audio post-processing operations.

Introducing paper: FMFCC-A: A Challenging Mandarin Dataset for Synthetic Speech Detection
POG
Full name: People On Grass

Object detection dataset featuring people walking on grass captured aboard a UAV. This data sets include precise meta data information about altitude, viewing angle and others.