The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class.
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Permuted MNIST is an MNIST variant that consists of 70,000 images of handwritten digits from 0 to 9, where 60,000 images are used for training, and 10,000 images for test. The difference of this dataset from the original MNIST is that each of the ten tasks is the multi-class classification of a different random permutation of the input pixels.
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CORe50 is a dataset designed for assessing Continual Learning techniques in an Object Recognition context.
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The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public domain imagery and public domain official building footprints.
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Collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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Concept-1K contains 1023 novel concepts from six domains, including economy, culture, science and technology, environment, education, and health and medical. It has 16653 training-test QA pairs corresponding to 16653 knowledge points from 1023 concepts. It is proposed for evaluating the forgetting in large language models and the effectiveness of incremental learning algorithms.
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HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly. This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation. Or where household objects or continual learning are of interest.
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Simulates unanticipated user needs in the deployment stage.