A common mistake in applied computer vision is to use a classical method (e.g. distanced-based watershed) to buoy your training set. You’ll end up with a comput

A common mistake in applied computer vision is to use a classical method (e.g. distanced-based watershed) to buoy your training set. You’ll end up with a computationally expensive method (e.g. a region-based convolutional neural network) that’s a poor replication of the classical method. The major advantage of learning-based methods is to go _beyond_ classical performance and make inferences comparable to the manually annotated image. There’s no shortcut.