Visualize images (and labels) from an image classification dataset.
tfds.show_examples( ds_info, ds, rows=3, cols=3, plot_scale=3.0, image_key=None )
Only works with datasets that have 1 image feature and optionally 1 label
feature (both inferred from
ds_info). Note the dataset should be unbatched.
Requires matplotlib to be installed.
This function is for interactive use (Colab, Jupyter). It displays and return a plot of (rows*columns) images from a tf.data.Dataset.
ds, ds_info = tfds.load('cifar10', split='train', with_info=True) fig = tfds.show_examples(ds_info, ds)
ds_info: The dataset info object to which extract the label and features info. Available either through
tf.data.Dataset. The tf.data.Dataset object to visualize. Examples should not be batched. Examples will be consumed in order until (rows * cols) are read or the dataset is consumed.
int, number of rows of the display grid.
int, number of columns of the display grid.
float, controls the plot size of the images. Keep this value around 3 to get a good plot. High and low values may cause the labels to get overlapped.
string, name of the feature that contains the image. If not set, the system will try to auto-detect it.