from datasets.office_31_PDA import * from datasets.office_home_PDA import * import argparse parser = argparse.ArgumentParser(description='All arguments for the program.') parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training.') parser.add_argument('--datase...
'../../../../_base_/datasets/coco.py' ] evaluation = dict(interval=10, metric='mAP', save_best='AP') optimizer = dict(type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1, constructor='LayerDecayOptimizerConstructor', paramwise_cfg=dict( num_layers=32, layer_decay_...
This is done to get rid of some of the pointless identifying information in datasets [25]. There are some passages in the book that are useless for figuring out if the information is accurate or not. Preprocessing is done to the dataset to clean it up and get rid of extraneous data. ...
A3Imagine a publishing company which does marketing for book and audio cassette versions. Create a class publication that stores the title (a string) and price (type float) of publications. From this class derive two classes: book which adds a page count (type int) and tape which adds a ...