AgML 0.4.5

This feature adds a couple of new features as well as bugfixes for the existing API.

Main Changes

• You can now use image classification models (agml.models.ClassificationModel) as image regression models by passing regression = True upon instantiation. This drops the final argmax computation and returns the softmax regression values.
• Pass a custom set of RGB values to agml.viz.set_colormap to use a custom colormap.
• A new preprocessing function has been added to agml.models.preprocessing: agml.models.preprocessing.imagenet_preprocess, which prepares images for input to an ImageNet-backend model (image classification, semantic segmentation).

Bugfixes

• The MeanAveragePrecision metric has been fixed, and no longer throws errors for empty predictions (or for early-stage training results).
• Custom object detection datasets can now be auto-loaded and classes automatically inferred without throwing an error.

Read the Full Changelog Here.