The image classification algorithm takes an image as input and outputs a probability for each provided class label. Training datasets must consist of images in .jpg, .jpeg, or .png format.

It is possible to create an MNIST image classification model by feeding the model one-dimensional vectors of 784 values. However, this approach isn't feasible for large images with millions of pixels, ...

Prior versions of the image captioning model took three seconds per training step on an Nvidia G20 GPU, but the version open sourced today can do the same task in a quarter of that time, or just 0 ...