The Oxford 102 Flower Dataset (often called Oxford Flowers-102) is a fine-grained image classification dataset created by the Visual Geometry Group (VGG) at the University of Oxford. It contains 102 flower categories commonly occurring in the United Kingdom. Each class has between 40 and 258 images, for a total of 8,189 images. The images exhibit large variation in scale, pose and illumination, and several classes are visually similar making the task challenging for classifiers. The dataset is split into training, validation and test sets: training and validation each contain 10 images per class (1,020 images each) and the test set contains the remaining 6,149 images (min 20 images per class). The dataset has been widely used for image classification and fine-grained visual categorization research and is available through multiple libraries and mirrors (official VGG homepage, TensorFlow Datasets, PyTorch torchvision and community Hugging Face dataset entries). Original dataset documentation and the authors' paper and thesis are hosted on the VGG (Oxford) website.
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