Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
## -----------------------------------------------------------------------------
# URLs_PETS()
## -----------------------------------------------------------------------------
# path = 'oxford-iiit-pet'
# path_anno = 'oxford-iiit-pet/annotations'
# path_img = 'oxford-iiit-pet/images'
# fnames = get_image_files(path_img)
## -----------------------------------------------------------------------------
# dls = ImageDataLoaders_from_name_re(
# path, fnames, pat='(.+)_\\d+.jpg$',
# item_tfms = RandomResizedCrop(460, min_scale=0.75), bs = 10,
# batch_tfms = list(aug_transforms(size = 299, max_warp = 0),
# Normalize_from_stats( imagenet_stats() )
# ),
# device = 'cuda'
# )
## -----------------------------------------------------------------------------
# dls %>% show_batch()
## -----------------------------------------------------------------------------
# learn = cnn_learner(dls, resnet50(), metrics = error_rate)
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(n_epoch = 8)
## -----------------------------------------------------------------------------
# learn$unfreeze()
#
# learn %>% fit_one_cycle(3, lr_max = slice(1e-6,1e-4))
## -----------------------------------------------------------------------------
# interp = ClassificationInterpretation_from_learner(learn)
# interp %>% most_confused()
## -----------------------------------------------------------------------------
# fls = list.files(paste(path,'images',sep = '/'),full.names = TRUE, recursive = TRUE)[c(250,500,700)]
# fls
#
# #[1] "oxford-iiit-pet/images/american_bulldog_142.jpg" "oxford-iiit-pet/images/american_pit_bull_terrier_188.jpg"
# #[3] "oxford-iiit-pet/images/basset_hound_188.jpg"
#
# result = learn %>% predict(fls)
#
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