Hi Mike!



 mkmitchell:

I’m using four 7 channel images in an attempt to classify wetlands from upland and water. Training, validation, and testing is showing very promising results with accuracy around 90% in all classes. When I save the model, load it, and classify one of the training images I’m getting around 60% accuracy.


At the risk of asking the obvious, what happens if you classify a
different training image (or one of your test or validation images)?
Could your “one of the training images” be an unlucky image?
As I understand it, you don’t data-augment the image for which
you get 60% accuracy.  (Do you data-augment your valiation
and test images?)  What happens if you train without data
augmentation?  Do you still see a discrepancy between your
training accuracy and your single-image accuracy?
Best regards.
K. Frank