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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) | |