09-small-models-road-to-the-top-part-2.ipynb

Kernel: python3

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

from fastkaggle import *

Going faster

comp = 'paddy-disease-classification'
path = setup_comp(comp, install='fastai "timm>=0.6.2.dev0"')
from fastai.vision.all import *
set_seed(42)

trn_path = Path('sml')

resize_images(path/'train_images', dest=trn_path, max_size=256, recurse=True)

dls = ImageDataLoaders.from_folder(trn_path, valid_pct=0.2, seed=42,
    item_tfms=Resize((256,192)))

dls.show_batch(max_n=3)

def train(arch, item, batch, epochs=5):
    dls = ImageDataLoaders.from_folder(trn_path, seed=42, valid_pct=0.2, item_tfms=item, batch_tfms=batch)
    learn = vision_learner(dls, arch, metrics=error_rate).to_fp16()
    learn.fine_tune(epochs, 0.01)
    return learn

learn = train('resnet26d', item=Resize(192),
              batch=aug_transforms(size=128, min_scale=0.75))

A ConvNeXt model

arch = 'convnext_small_in22k'

learn = train(arch, item=Resize(192, method='squish'),
              batch=aug_transforms(size=128, min_scale=0.75))

Preprocessing experiments

learn = train(arch, item=Resize(192),
              batch=aug_transforms(size=128, min_scale=0.75))

dls = ImageDataLoaders.from_folder(trn_path, valid_pct=0.2, seed=42,
    item_tfms=Resize(192, method=ResizeMethod.Pad, pad_mode=PadMode.Zeros))
dls.show_batch(max_n=3)

learn = train(arch, item=Resize((256,192), method=ResizeMethod.Pad, pad_mode=PadMode.Zeros),
      batch=aug_transforms(size=(171,128), min_scale=0.75))

Test time augmentation

valid = learn.dls.valid
preds,targs = learn.get_preds(dl=valid)

error_rate(preds, targs)

learn.dls.train.show_batch(max_n=6, unique=True)

tta_preds,_ = learn.tta(dl=valid)

error_rate(tta_preds, targs)

Scaling up

trn_path = path/'train_images'

learn = train(arch, epochs=12,
              item=Resize((480, 360), method=ResizeMethod.Pad, pad_mode=PadMode.Zeros),
              batch=aug_transforms(size=(256,192), min_scale=0.75))

tta_preds,targs = learn.tta(dl=learn.dls.valid)
error_rate(tta_preds, targs)

Submission

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

preds,_ = learn.tta(dl=tst_dl)

idxs = preds.argmax(dim=1)

vocab = np.array(learn.dls.vocab)
results = pd.Series(vocab[idxs], name="idxs")

ss = pd.read_csv(path/'sample_submission.csv')
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', 'convnext small 256x192 12 epochs tta', comp)

# This is what I use to push my notebook from my home PC to Kaggle

if not iskaggle:
    push_notebook('jhoward', 'small-models-road-to-the-top-part-2',
                  title='Small models: Road to the Top, Part 2',
                  file='small-models-road-to-the-top-part-2.ipynb',
                  competition=comp, private=True, gpu=True)

Conclusion