from fastai import *
from fastai.vision import *
from fastai.widgets import DatasetFormatter, ImageCleaner
fastai offers several widgets to support the workflow of a deep learning practitioner. The purpose of the widgets are to help you organize, clean, and prepare your data for your model. Widgets are separated by data type.
The DatasetFormatter class prepares your image dataset for widgets by returning a formatted DatasetTfm based on the DatasetType specified. Use from_toplosses to grab the most problematic images directly from your learner. Optionally, you can restrict the formatted dataset returned to n_imgs.
Specify the DatasetType you'd like to process:
path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
learner = create_cnn(data, models.resnet18, metrics=[accuracy])
ds, idxs = DatasetFormatter().from_toplosses(learner, ds_type=DatasetType.Valid)
ImageDeleter is for cleaning up images that don't belong in your dataset. It renders images in a row and gives you the opportunity to delete the file from your file system.
ImageCleaner(ds, idxs)
HBox(children=(VBox(children=(Image(value=b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x01\x00d\x00d\x00\x00\xff…
Button(button_style='primary', description='Next Batch', layout=Layout(width='auto'), style=ButtonStyle())
<fastai.widgets.image_cleaner.ImageCleaner at 0x7fafb02a3630>