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, ...

This small change will help us improve the model accuracy from a 0.66 combined test accuracy to 0.89 while using the same dataset and no custom coding! Here is our plan of action: We will rebuild ...

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 ...

An example of an image classification problem is to identify a photograph of an animal as a "dog" or "cat" or "monkey." The two most common approaches for image classification are to use a standard ...