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  2. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    The architecture of vision transformer. An input image is divided into patches, each of which is linearly mapped through a patch embedding layer, before entering a standard Transformer encoder. A vision transformer (ViT) is a transformer designed for computer vision. [1] A ViT decomposes an input image into a series of patches (rather than text ...

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Images Classification 2009 [18] [36] A. Krizhevsky et al. CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [18] [36] A. Krizhevsky et al. CINIC-10 Dataset A unified contribution of CIFAR-10 and Imagenet with 10 classes, and 3 splits.

  4. CIFAR-10 - Wikipedia

    en.wikipedia.org/wiki/CIFAR-10

    The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]

  5. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    A common algorithmic metric for assessing image quality and diversity is the Inception Score (IS), which is based on the distribution of labels predicted by a pretrained Inceptionv3 image classification model when applied to a sample of images generated by the text-to-image model. The score is increased when the image classification model ...

  6. Contextual image classification - Wikipedia

    en.wikipedia.org/.../Contextual_image_classification

    Contextual image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. "Contextual" means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood.

  7. One-shot learning (computer vision) - Wikipedia

    en.wikipedia.org/wiki/One-shot_learning...

    One-shot learning is an object categorization problem, found mostly in computer vision.Whereas most machine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.

  8. Image registration - Wikipedia

    en.wikipedia.org/wiki/Image_registration

    Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. [1] It is used in computer vision, medical imaging, [2] military automatic target recognition, and compiling and analyzing images and data from ...

  9. Image analysis - Wikipedia

    en.wikipedia.org/wiki/Image_analysis

    Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face .