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Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] ... image classification, object detection, and segmentation.
Images Classification, Segmentation 2020 [261] ... This is the one associated with the dataset card in Hugging Face. 2021 [344] Wei et al. Cybersecurity.
Color images of faces at various angles. Location of facial features extracted. Coordinates of features given. 4,160 Images, text Classification, face recognition 2011 [91] [92] M. Grgic et al. Yale Face Database Faces of 15 individuals in 11 different expressions. Labels of expressions. 165 Images Face recognition 1997 [93] [94] J. Yang et al.
CLIP can perform zero-shot image classification tasks. This is achieved by prompting the text encoder with class names and selecting the class whose embedding is closest to the image embedding. For example, to classify an image, they compared the embedding of the image with the embedding of the text "A photo of a {class}.", and the {class} that ...
The Fashion MNIST dataset is a large freely available database of fashion images that is commonly used for training and testing various machine learning systems. [1] [2] Fashion-MNIST was intended to serve as a replacement for the original MNIST database for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.
Open-source machine translation models have paved the way for multilingual support in applications across industries. Hugging Face's MarianMT is a prominent example, providing support for a wide range of language pairs, becoming a valuable tool for translation and global communication. [63]
The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. [2] [3] The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set ...
This difference is then used to categorize subsections of an image. For example, with a human face, it is a common observation that among all faces the region of the eyes is darker than the region of the cheeks. Therefore, a common Haar feature for face detection is a set of two adjacent rectangles that lie above the eye and the cheek region.