AugMix() explained: no arguments, severity argument (1-3), mixture_width argument (2-3) and examples with OxfordIIITPet dataset.

AugMix() explained: no args & full arg, severity (2), mixture_width (1&2) and examples with OxfordIIITPet dataset.

RandAugment() explained: num_ops (default:2), magnitude (default:9), num_magnitude_bins (default:31), interpolation (default:NEAREST), fill (default:None) for image augmentation.

RandAugment() explained: num_ops (default:2), magnitude (default:9), num_magnitude_bins (default:31), interpolation (default:NEAREST), fill (default:None) for image augmentation.

FiveCrop() crops an image into 5 parts. Size argument can be int or tuple/list(int) with height and width.

CenterCrop() crops an image centering on it. The size argument can be a single value (int or tuple/list(int)) for [size, size] or a tuple/list with 1 or 2 elements for [height, width].

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). p must be 0 <= x <= 1. OxfordIIITPet dataset is used to test RandomInvert().

Grayscale() converts images to grayscale. OxfordIIITPet() dataset requires 1 or 3 output channels. Use Grayscale(num_output_channels=1) for 1-channel images and num_output_channels=3 for 3-channel images.

CenterCrop() crops images centering on them. It takes a size argument which can be an int or tuple/list of ints. A tensor must be 2D or 3D. V1 is deprecated, use v2 instead.

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). Use v2 for better performance.

Grayscale() converts images to grayscale. OxfordIIITPet() dataset requires 1 or 3 output channels. Use Grayscale(num_output_channels=1) for grayscale conversion.

FiveCrop() crops an image into 5 parts. Size can be int or tuple/list(int) with 1 <= x. Use OxfordIIITPet() for dataset and torchvision.transforms.v2.FiveCrop() for transformation.

JPEG compression applied to images with quality range [0, 100] using torchvision.transforms.v2.JPEG. Example usage: JPEG(quality=[50, 100])

ColorJitter(brightness=brightness_value, contrast=contrast_value, saturation=saturation_value, hue=hue_value)

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) and p (probability of inversion). Use v2 for better performance.

RandomInvert() inverts images with a probability of 0 <= p <= 1. OxfordIIITPet() dataset uses RandomInvert() for data augmentation. Use v2 for better results.

RandomInvert() can randomly blur an image. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). p must be 0 <= x <= 1.

GaussianBlur() can randomly blur an image. It has kernel_size and sigma arguments. Use GaussianBlur(kernel_size=1) for a simple blur.

RandomCrop() without pad_if_needed argument explained. It randomly crops images with optional padding and fill values.

CenterCrop() crops images centering on them. It takes a size argument which can be an int or tuple/list of ints. The size must have at least one element and all elements must be >=1.

RandomRotation(degrees=d, interpolation=ip, expand=e, center=c, fill=f)

RandomHorizontalFlip() flips an image randomly and horizontally. It takes 2 args: img (PIL Image or tensor) & p (probability of flip). Example usage: RandomHorizontalFlip(p=0.5)

RandomVerticalFlip() flips an image randomly and vertically with a probability of 0 <= x <= 1. Use img as input and p for the flip probability. V2 is recommended over V1.

Use PyTorch's `RandomResizedCrop` transformation to randomly crop images. Define functions like `show_images` and `show_images2` to apply transformations with customizable size, scale, ratio, and interpolation.

FiveCrop() crops an image into 5 parts. Use size=(100, 100) or size=100 for initialization.

CenterCrop() crops images centering on them. Use size=(100, 100) or size=100 for square crops.

Update Ubuntu, check Python version (e.g. `python3 --version`), install python3.x-venv & create virtual env (`python3 -m venv venv && . venv/bin/activate`). Install PyTorch with CUDA 11.8 and JupyterLab.

FiveCrop() crops an image into 5 parts. Use FiveCrop(size=(100, 100)) or FiveCrop(size=100) for a square crop. For non-square crops, use FiveCrop(size=[500, 394]).

Resize images with torchvision.transforms.v2.Resize. Optional arguments: size (int or tuple/list), interpolation (InterpolationMode), max_size (int), antialias (bool).

CenterCrop() crops zero or more images, centering on them. Use size=(100, 100) for a square crop or size=600 for a rectangular crop.

Python script generates images using `show_images2` but lacks clarity & documentation. Redundant calls, magic numbers & unclear purpose hinder understanding. More context needed for a helpful answer.

Padding images is a technique used to make them consistent in size for tasks like image classification, object detection & segmentation. It adds zeros around edges to ensure entire objects are visible.

RandomPerspective() can do perspective transformation for zero or more images. It has 4 arguments: distortion_scale (default:0.5), p (default:0.5), interpolation (default:BILINEAR) and fill (default:0).

Update Ubuntu, check Python version (e.g. `python3 --version`), install python3.x-venv & create virtual env (`python3 -m venv venv && . venv/bin/activate`). Install PyTorch with CUDA 11.8 and JupyterLab.

Modified Python script fixes issue with accessing non-existent indices in datasets. Checks for index existence before displaying images, displaying default image if index doesn't exist.

Code displays images with annotations from various datasets using matplotlib & COCO API. Issues: undefined variables, missing imports, unclear data structure. Improve by defining data loading process, consistent imports & documenting data structure.

Exploring popular datasets in computer vision: ImageNet, LSUN, MS COCO, Fashion-MNIST & more. Learn about their features, uses & PyTorch implementations.

StanfordCars() dataset can be used with torchvision.datasets. It requires root path and split type (train/test). Download is optional but set to False due to broken URL.

CocoCaptions() explained using MS COCO dataset with train2017, val2017, and unlabeled2017. CocoDetection() also covered for train2014, val2014, test2017, train2017, and panoptic_train2017.

Consistent naming conventions, type hints, docstrings & error handling improve code readability & robustness. Updated `show_images1()` & `show_images2()` functions demonstrate these improvements.

pms_stf_train2017_data` & `pms_stf_val2017_data` undefined in code. Load COCO data with `pycocotools` library, assign to variables before accessing. Example: `coco = COCO('path_to_your_dataset/instances_train2017.json')

Modified code to display COCO dataset images with annotations: iterates over indices directly, removes unnecessary line. Works for cap_train2017_data, cap_val2017_data, test2017_data, testdev2017_data.

Displaying images with various types of annotations using matplotlib. The `show_images` function takes in a dataset and displays images along with their corresponding annotations, customizable to attribute, identity, bounding box or landmark data.

all() checks if all elements in a tensor are True. Returns 0D or more D tensor of zero or more elements. Example: `torch.all(input=torch.tensor(True))` returns `tensor(True)

any() checks if any elements of a tensor are True, returning the 0D or more D tensor of zero or more elements. It can be used with torch or a tensor, and has optional arguments for dim and keepdim. An empty tensor returns False.

atleast_1d() in PyTorch: Ensures 1D or higher dimensionality for tensors. Returns a tensor or tuple of tensors with at least 1D structure, depending on input arguments.

remainder() in PyTorch performs modulo operation on tensors or scalars, returning the remainder of input divided by other. It can handle zero or more elements and supports torch or tensor inputs.

fmod() in PyTorch performs modulo operation on tensors or scalars, returning the remainder of division with same sign as original tensor. Example: `torch.fmod(input=tensor1, other=tensor2)` or `tensor1.fmod(other=tensor2)`.

arange() creates a 1D tensor of zero or integers or floating-point numbers between start and end-1. With torch, it has optional arguments: start, end, step, dtype, device, requires_grad, and out.

squeeze() removes 1D dimensions from tensors. Use torch.squeeze() or tensor.squeeze(). Specify dim to remove specific dimensions. Examples: `torch.squeeze(input=my_tensor)` and `my_tensor.squeeze(dim=(0, 3))`.

range() creates a sequence of numbers. zip() pairs values from multiple sequences. enumerate() adds indices to iterables.

QMNIST dataset explained. QMNIST() loads 6 datasets: train, test, test10k, test50k, nist and FashionMNIST(). It returns images and labels.

MNIST dataset explained. MNIST() can use MNIST dataset with 5 arguments: root, train, transform, target_transform, download.

KMNIST() explained: a dataset for handwritten Kanji character recognition. Use KMNIST() with torchvision.datasets to load and preprocess data.

RandomVerticalFlip() flips images vertically with a probability of 0.5 by default. It can be used to augment datasets like OxfordIIITPet().

Fashion-MNIST dataset can be used with FashionMNIST() function. It requires root path and optional arguments for train data, transform, target transform, and download.

CIFAR100() loads CIFAR-100 dataset. Args: root(str/pathlib.Path), train(bool), transform(callable), target_transform(callable), download(bool). Default: train=True, transform=None, target_transform=None, download=False.

CIFAR10() uses CIFAR-10 dataset with 5 args: root(str), train(bool), transform(callable), target_transform(callable), download(bool).

Batch Gradient Descent, Mini-Batch Gradient Descent and Stochastic Gradient Descent explained with PyTorch examples.

Flatten() and ravel() explained: removing dimensions from tensors with optional start_dim and end_dim arguments. Examples in PyTorch.