Raisin_Dataset

Ilkay Cinar, Murat Kokl and Sakir Tasdemi(2020)
provide a dataset consisting of 2 varieties of Turkish raisins, with 450
samples of each type. The dataset contains 7 morphological features,
extracted from images taken of the Raisins. The goal is to classify to one of
the two types of Raisins.

datasets

Latent binary Bayesian neural networks (LBBNNs) are implemented using
'torch', an R interface to the LibTorch backend. Supports mean-field variational
inference as well as flexible variational posteriors using normalizing flows.
The standard LBBNN implementation follows Hubin and Storvik (2024) <doi:10.3390/math12060788>,
using the local reparametrization trick as in Skaaret-Lund et al. (2024)
<https://openreview.net/pdf?id=d6kqUKzG3V>. Input-skip connections are also supported,
as described in Høyheim et al. (2025) <doi:10.48550/arXiv.2503.10496>.

Lars Skaaret-Lund

LBBNN

Latent Binary Bayesian Neural Networks Using 'torch'

Aliaksandr Hubin

Eirik Høyheim

Raisin_Dataset function

this data frame has 900 rows and the following 8 columns:<dl>
<dt>Area</dt>
<dd>Number of pixels within the boundary</dd><dt>MajorAxisLength</dt>
<dd>Length of the main axis</dd><dt>MinorAxisLength</dt>
<dd>Length of the small axis</dd><dt>Eccentricity</dt>
<dd>Measure of the eccentricity of the ellipse</dd><dt>ConvexArea</dt>
<dd>The number of pixels of the
smallest convex shell of the region formed by the raisin grain</dd><dt>Extent</dt>
<dd>Ratio of the region formed by the
raisin grain to the total pixels in the bounding box</dd><dt>Perimeter</dt>
<dd>distance between the boundaries of the
raisin grain and the pixels around it</dd><dt>Class</dt>
<dd>Kecimen or Besni raisin.</dd>
</dl>

Format

Raisins Dataset — Raisin_Dataset

this data frame has 900 rows and the following 8 columns:<dl>
<dt>Area</dt>
<dd>Number of pixels within the boundary</dd>

<dt>MajorAxisLength</dt>
<dd>Length of the main axis</dd>

<dt>MinorAxisLength</dt>
<dd>Length of the small axis</dd>

<dt>Eccentricity</dt>
<dd>Measure of the eccentricity of the ellipse</dd>

<dt>ConvexArea</dt>
<dd>The number of pixels of the
smallest convex shell of the region formed by the raisin grain</dd>

<dt>Extent</dt>
<dd>Ratio of the region formed by the
raisin grain to the total pixels in the bounding box</dd>

<dt>Perimeter</dt>
<dd>distance between the boundaries of the
raisin grain and the pixels around it</dd>

<dt>Class</dt>
<dd>Kecimen or Besni raisin.</dd>


</dl>

Raisin_Dataset: Raisins Dataset

Description

Usage

Arguments

Format