Parameters

• name — Dataset name, empty is okay for folder based datasets
• root — Root folder of dataset (All)
• split — Dataset split (All)
• search_split — Search for split specific child fold from root so one can specify imagenet/ instead of /imagenet/val, etc on cmd line / config. (Folder, Torch)
• class_map — Specify class -> index mapping via text file or dict (Folder)
• load_bytes — Load data, return images as undecoded bytes (Folder)
• download — Download dataset if not present and supported (HFIDS, TFDS, Torch)
• is_training — Create dataset in train mode, this is different from the split. For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS, HFIDS)
• batch_size — Batch size hint for iterable datasets (TFDS, WDS, HFIDS)
• seed — Seed for iterable datasets (TFDS, WDS, HFIDS)
• repeats — Dataset repeats per iteration i.e. epoch (TFDS, WDS, HFIDS)
• input_img_mode — Input image color conversion mode e.g. ‘RGB’, ‘L’ (folder, TFDS, WDS, HFDS, HFIDS)
• trust_remote_code — Trust remote code in Hugging Face Datasets if True (HFDS, HFIDS)
• **kwargs — Other args to pass through to underlying Dataset and/or Reader classes

Parameters

• dataset — The image dataset to load.
• input_size — Target input size (channels, height, width) tuple or size scalar.
• batch_size — Number of samples in a batch.
• is_training — Return training (random) transforms.
• no_aug — Disable augmentation for training (useful for debug).
• re_prob — Random erasing probability.
• re_mode — Random erasing fill mode.
• re_count — Number of random erasing regions.
• re_split — Control split of random erasing across batch size.
• scale — Random resize scale range (crop area, < 1.0 => zoom in).
• ratio — Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
• hflip — Horizontal flip probability.
• vflip — Vertical flip probability.
• color_jitter — Random color jitter component factors (brightness, contrast, saturation, hue). Scalar is applied as (scalar,) * 3 (no hue).
• color_jitter_prob — Apply color jitter with this probability if not None (for SimlCLR-like aug
• grayscale_prob — Probability of converting image to grayscale (for SimCLR-like aug).
• gaussian_blur_prob — Probability of applying gaussian blur (for SimCLR-like aug).
• auto_augment — Auto augment configuration string (see auto_augment.py).
• num_aug_repeats — Enable special sampler to repeat same augmentation across distributed GPUs.
• num_aug_splits — Enable mode where augmentations can be split across the batch.
• interpolation — Image interpolation mode.
• mean — Image normalization mean.
• std — Image normalization standard deviation.
• num_workers — Num worker processes per DataLoader.
• distributed — Enable dataloading for distributed training.
• crop_pct — Inference crop percentage (output size / resize size).
• crop_mode — Inference crop mode. One of [‘squash’, ‘border’, ‘center’]. Defaults to ‘center’ when None.
• crop_border_pixels — Inference crop border of specified # pixels around edge of original image.
• collate_fn — Override default collate_fn.
• pin_memory — Pin memory for device transfer.
• fp16 — Deprecated argument for half-precision input dtype. Use img_dtype.
• img_dtype — Data type for input image.
• device — Device to transfer inputs and targets to.
• use_prefetcher — Use efficient pre-fetcher to load samples onto device.
• use_multi_epochs_loader —
• persistent_workers — Enable persistent worker processes.
• worker_seeding — Control worker random seeding at init.
• tf_preprocessing — Use TF 1.0 inference preprocessing for testing model ports.

Parameters

• input_size — Target input size (channels, height, width) tuple or size scalar.
• is_training — Return training (random) transforms.
• no_aug — Disable augmentation for training (useful for debug).
• train_crop_mode — Training random crop mode (‘rrc’, ‘rkrc’, ‘rkrr’).
• scale — Random resize scale range (crop area, < 1.0 => zoom in).
• ratio — Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
• hflip — Horizontal flip probability.
• vflip — Vertical flip probability.
• color_jitter — Random color jitter component factors (brightness, contrast, saturation, hue). Scalar is applied as (scalar,) * 3 (no hue).
• color_jitter_prob — Apply color jitter with this probability if not None (for SimlCLR-like aug).
• grayscale_prob — Probability of converting image to grayscale (for SimCLR-like aug).
• gaussian_blur_prob — Probability of applying gaussian blur (for SimCLR-like aug).
• auto_augment — Auto augment configuration string (see auto_augment.py).
• interpolation — Image interpolation mode.
• mean — Image normalization mean.
• std — Image normalization standard deviation.
• re_prob — Random erasing probability.
• re_mode — Random erasing fill mode.
• re_count — Number of random erasing regions.
• re_num_splits — Control split of random erasing across batch size.
• crop_pct — Inference crop percentage (output size / resize size).
• crop_mode — Inference crop mode. One of [‘squash’, ‘border’, ‘center’]. Defaults to ‘center’ when None.
• crop_border_pixels — Inference crop border of specified # pixels around edge of original image.
• tf_preprocessing — Use TF 1.0 inference preprocessing for testing model ports
• use_prefetcher — Pre-fetcher enabled. Do not convert image to tensor or normalize.
• normalize — Normalization tensor output w/ provided mean/std (if prefetcher not used).
• separate — Output transforms in 3-stage tuple.