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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) | |