renumics
Datasets
All datasets matching “renumics”cifar100-enrichedThe CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).dcase23-task2-enriched
Dataset Card for the Enriched "DCASE 2023 Challenge Task 2 Dataset".
Dataset Summary
Data-centric AI principles have become increasingly important for real-world use cases. At Renumics we believe that classical benchmark datasets and competitions should be extended to reflect this development.
This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps… See the full description on the dataset page: https://huggingface.co/datasets/renumics/dcase23-task2-enriched.cifar10-enrichedThe CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
per class. There are 50000 training images and 10000 test images.
This version if CIFAR-10 is enriched with several metadata such as embeddings, baseline results and label error scores.song-describer-datasetThis is a mirror to the example dataset "The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation" paper by Manco et al.
Project page on Github: https://github.com/mulab-mir/song-describer-dataset
Dataset on Zenodoo: https://zenodo.org/records/10072001
Explore the dataset on your local machine:
import datasets
from renumics import spotlight
ds = datasets.load_dataset('renumics/song-describer-dataset')
spotlight.show(ds)
speech_commands_enrichedThis is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail at
[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
64,727 audio files.
In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
from unrecognized ones.
This version is not yet supported.
The `_silence_` class contains a set of longer audio clips that are either recordings or
a mathematical simulation of noise.speech_commands_enrichment_only
Dataset Card for SpeechCommands
Dataset Summary
📊 Data-centric AI principles have become increasingly important for real-world use cases.At Renumics we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the… See the full description on the dataset page: https://huggingface.co/datasets/renumics/speech_commands_enrichment_only.