The 'fastai' <https://docs.fast.ai/index.html> library
simplifies training fast and accurate neural networks
using modern best practices. It is based on research
in to deep learning best practices undertaken
at 'fast.ai', including 'out of the box' support
for vision, text, tabular, audio, time series, and
collaborative filtering models.

Interface to 'fastai'

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7

Randomly selects a rectangle region in an image and randomizes its pixels.

RandomErasing(p = 0.5, sl = 0, sh = 0.3, min_aspect = 0.3, max_count = 1)

<table summary="R argblock">
<tr valign="top">
<td><code>p</code></td>
<td>
probability
</td>
</tr>
<tr valign="top">
<td><code>sl</code></td>
<td>
sl
</td>
</tr>
<tr valign="top">
<td><code>sh</code></td>
<td>
sh
</td>
</tr>
<tr valign="top">
<td><code>min_aspect</code></td>
<td>
minimum aspect
</td>
</tr>
<tr valign="top">
<td><code>max_count</code></td>
<td>
maximum count
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7#docs#RandomErasing

RandomErasing

Description

Usage

Arguments

Value

fastai

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`p`	probability
`sl`	sl
`sh`	sh
`min_aspect`	minimum aspect
`max_count`	maximum count