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The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
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.
Talk. : MNIST database. This is the talk page for discussing improvements to the MNIST database article. This is not a forum for general discussion of the article's subject. Assume good faith. Be polite and avoid personal attacks. Be welcoming to newcomers. Seek dispute resolution if needed.
Multi-modal dataset for obstacle detection in agriculture including stereo camera, thermal camera, web camera, 360-degree camera, lidar, radar, and precise localization. Classes labelled geographically. >400 GB of data. Images and 3D point clouds. Classification, object detection, object localization.
LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1][2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3] ImageNet contains more than 20,000 ...
Download Data. We also urge universities to post the reports they must make to the NCAA in an easy-to-find location on their websites. Very few do so. This seems to us a necessary first step toward better communicating with students and parents about the true cost of supporting intercollegiate sports. —Ben Hallman and Shane Shifflett. Methodology
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