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Data

timm.data.create_dataset

< >

( name root split = 'validation' search_split = True class_map = None load_bytes = False is_training = False download = False batch_size = None seed = 42 repeats = 0 **kwargs )

Dataset factory method

In parenthesis after each arg are the type of dataset supported for each arg, one of:

  • folder - default, timm folder (or tar) based ImageDataset
  • torch - torchvision based datasets
  • HFDS - Model Database Datasets
  • TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
  • WDS - Webdataset
  • all - any of the above

timm.data.create_loader

< >

( dataset input_size batch_size is_training = False use_prefetcher = True no_aug = False re_prob = 0.0 re_mode = 'const' re_count = 1 re_split = False scale = None ratio = None hflip = 0.5 vflip = 0.0 color_jitter = 0.4 auto_augment = None num_aug_repeats = 0 num_aug_splits = 0 interpolation = 'bilinear' mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) num_workers = 1 distributed = False crop_pct = None crop_mode = None collate_fn = None pin_memory = False fp16 = False img_dtype = torch.float32 device = device(type='cuda') tf_preprocessing = False use_multi_epochs_loader = False persistent_workers = True worker_seeding = 'all' )

timm.data.create_transform

< >

( input_size is_training = False use_prefetcher = False no_aug = False scale = None ratio = None hflip = 0.5 vflip = 0.0 color_jitter = 0.4 auto_augment = None interpolation = 'bilinear' mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) re_prob = 0.0 re_mode = 'const' re_count = 1 re_num_splits = 0 crop_pct = None crop_mode = None tf_preprocessing = False separate = False )

timm.data.resolve_data_config

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( args = None pretrained_cfg = None model = None use_test_size = False verbose = False )