With a special thanks to NerdFarmer, we have another sponsored episode on classifying leaf diseases. This time, we focused on trying to simplify the data and the model to make it a little more approachable. Previously, I squared up images by cutting to the smaller dimension and rescaling to 224x224 (thinking maybe transfer learning would happen later). This time, I decided not to throw away any part of images. So I padded the smaller dimension to equal the larger. To get them all on the same size, I still rescaled, now to 64x64. So rather than holding 2GB of data in memory, it was only about 256 MB; much faster to work with. During the stream itself, we even reached 66% accuracy.

After the stream, a fan commented that he used the same model to get about 100% accuracy! While this is great to hear, we may be overfitting the model. At this point, we should select a random subset of the training data to use for validation. I fear the accuracy may not be so stellar.