The fastai library
simplifies training fast and accurate neural nets using modern best
practices. See the fastai website to get started. The library is based
on research into deep learning best practices undertaken at
fast.ai, and includes “out of the box” support for
vision, text, tabular, and
collab (collaborative filtering) models.
Data augmentation plays a huge role while working on Computer Vision task. Because the proper image transformation can drastically improve the generalization while building a deep learning model.
Read image:
img = fastai::Image_create('files/cat.jpeg')Plot it:
<center>
<img src="images/cat.png" alt="_" style="width: 350px;"/>
</center>
img_res = list(img, img$flip_lr())
titles = c('original', 'flipped')
c(fig, axs) %<-% subplots(1,2)
for (i in 1:2) {
img_res[[i]] %>% show_image(ax = axs[[i]],
title=titles[i])
}
img %>% plot(dpi = 250)<center>
<img src="images/flip.png" alt="_" style="width: 350px;"/>
</center>
c(fig, axs) %<-% subplots(2, 4)
for (i in 1:8) {
show_image(DihedralItem(p = 1.)(img, split_idx = 0), ctx = axs[[i]])
}
img %>% plot(dpi = 250)<center>
<img src="images/dihedral.png" alt="_" style="width: 350px;"/>
</center>
sz = c(300L, 500L, 700L)
size = paste('Size', sz)
c(fig, axs) %<-% subplots(1, 3, figsize = c(12, 4))
for (i in 1:3) {
show_image(img$crop_pad(sz[i]), ctx = axs[[i]], title = size[i])
}
img %>% plot(dpi = 250)<center>
<img src="images/crop.png" alt="_" style="width: 350px;"/>
</center>
pad_modes = c('border', 'reflection', 'zeros')
c(fig, axs) %<-% subplots(1, 3, figsize = c(12, 4))
for (i in 1:3) {
show_image(img$crop_pad(c(600L,700L), pad_mode = pad_modes[i]),
ctx = axs[[i]], title = pad_modes[i])
}
img %>% plot(dpi = 250)<center>
<img src="images/modes.png" alt="_" style="width: 350px;"/>
</center>
c(fig, axs) %<-% subplots(1, 3, figsize = c(12, 4))
ff = RandomCrop(100)
for (i in 1:3) {
show_image(ff(img), ctx = axs[[i]])
}
img %>% plot(dpi = 250)<center>
<img src="images/random_crop.png" alt="_" style="width: 350px;"/>
</center>
c(fig, axs) %<-% subplots(1, 3, figsize = c(12, 4))
ff = RandomCrop(100L)
for (i in 1:3) {
show_image(ff(img, split_idx = 1L), ctx = axs[[i]])
}
img %>% plot(dpi = 250)<center>
<img src="images/center_crop.png" alt="_" style="width: 350px;"/>
</center>
resize = c('squish', 'pad', 'crop')
c(fig, axs) %<-% subplots(1, 3, figsize = c(12, 4))
for (i in 1:3) {
rsz = Resize(256, method = resize[i])
show_image(rsz(img, split_idx = 0L), ctx = axs[[i]], title = resize[i])
}
img %>% plot(dpi = 250)<center>
<img src="images/resize.png" alt="_" style="width: 350px;"/>
</center>
c(fig, axs) %<-% subplots(3, 3, figsize = c(9, 9))
ff = RandomResizedCrop(100)
for (i in 1:9) {
show_image(ff(img), ctx = axs[[i]])
}
img %>% plot(dpi = 250)<center>
<img src="images/random_resize.png" alt="_" style="width: 350px;"/>
</center>