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from fastbook import *
from fastai.vision.widgets import *
## Gathering Data
path = Path('data_devices')
device_types = 'laptop','desktop pc','mobile phone'
if not path.exists():
path.mkdir()
for o in device_types:
dest = (path/o)
dest.mkdir(exist_ok=True)
results = search_images_ddg(f'{o}', max_images=100)
# results = search_images_bing(key, f'{o} bear')
# download_images(dest, urls=results.attrgot('contentUrl'))
download_images(dest, urls=results)
fns = get_image_files(path)
failed = verify_images(fns)
failed.map(Path.unlink);
#*
## From Data to DataLoaders
devices = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pct=0.2, seed=42),
get_y=parent_label,
item_tfms=Resize(128))
dls = devices.dataloaders(path)
### Data Augmentation
## Training Your Model, and Using It to Clean Your Data
devices = devices.new(
item_tfms=RandomResizedCrop(224, min_scale=0.5),
batch_tfms=aug_transforms())
dls = devices.dataloaders(path)
learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(4)
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()
## Turning Your Model into an Online Application
### Using the Model for Inference
learn.export()
path = Path()
path.ls(file_exts='.pkl')
learn_inf = load_learner(path/'export.pkl')
query02 = "dell inspiron"
urls = search_images_ddg(query02, max_images = 5 )
dest = f'data_test/{query02}.jpg'
download_url(urls[0], dest)
learn_inf.predict(dest)
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