08-first-steps-road-to-the-top-part-1.ipynb

Kernel: python3

# install fastkaggle if not available
try: import fastkaggle
except ModuleNotFoundError:
    !pip install -Uq fastkaggle

from fastkaggle import *

Getting set up

comp = 'paddy-disease-classification'

path = setup_comp(comp, install='fastai "timm>=0.6.2.dev0"')

path

from fastai.vision.all import *
set_seed(42)

path.ls()

Looking at the data

trn_path = path/'train_images'
files = get_image_files(trn_path)

img = PILImage.create(files[0])
print(img.size)
img.to_thumb(128)

from fastcore.parallel import *

def f(o): return PILImage.create(o).size
sizes = parallel(f, files, n_workers=8)
pd.Series(sizes).value_counts()

dls = ImageDataLoaders.from_folder(trn_path, valid_pct=0.2, seed=42,
    item_tfms=Resize(480, method='squish'),
    batch_tfms=aug_transforms(size=128, min_scale=0.75))

dls.show_batch(max_n=6)

Our first model

learn = vision_learner(dls, 'resnet26d', metrics=error_rate, path='.').to_fp16()

learn.lr_find(suggest_funcs=(valley, slide))

learn.fine_tune(3, 0.01)

Submitting to Kaggle

ss = pd.read_csv(path/'sample_submission.csv')
ss

tst_files = get_image_files(path/'test_images').sorted()
tst_dl = dls.test_dl(tst_files)

probs,_,idxs = learn.get_preds(dl=tst_dl, with_decoded=True)
idxs

dls.vocab

mapping = dict(enumerate(dls.vocab))
results = pd.Series(idxs.numpy(), name="idxs").map(mapping)
results

ss['label'] = results
ss.to_csv('subm.csv', index=False)
!head subm.csv

if not iskaggle:
    from kaggle import api
    api.competition_submit_cli('subm.csv', 'initial rn26d 128px', comp)

Conclusion

Addendum

if not iskaggle:
    push_notebook('jhoward', 'first-steps-road-to-the-top-part-1',
                  title='First Steps: Road to the Top, Part 1',
                  file='first-steps-road-to-the-top-part-1.ipynb',
                  competition=comp, private=False, gpu=True)