Test it on the Peltarion Platform
A platform to build and deploy deep learning projects.
Even if you’re not an AI superstar.
The MIT License (MIT). Copyright (c) 2015 Juan Cazala . Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
RGB, 28 x 28 pixels 3-channel images (28x28x3). Used in the tutorial Deploy an operational AI model: Zipped RGB MNIST bundle
Grayscale, 28 x 28 pixels 1-channel images (28x28x1). Used in the tutorial Denoising images: Zipped Grayscale MNIST bundle (Training data)
For original grayscale (28x28x1) go to The MNIST database.