Paper details clarification about deepweeds HOT 1 CLOSED

Hi Kleyson. Thanks for your comment. Here's my (very) late response to your questions.

1. Transfer learning is not limited to fine-tuning just the "top" of the pre-trained network. You have the option of freezing or not freezing any combination of layers during the transfer learning process. As nicely described in Stanford's CS231n course notes on Transfer Learning (http://cs231n.github.io/transfer-learning/), your choice for either fine tuning the top of the network, or using the pre-trained weights as a starting point to fine tune the entire network depends on the size of the new dataset and how different the new learning problem is. Because of the sufficient size of our dataset and the difference in learning nature between specific weeds and varied ImageNet classes, I decided to fine-tune through the entire network.

2. You are correct, there is major class imbalance in this dataset. However, this class imbalance is true for the nature of the learning problem for weed classification in the field. With no method of balancing the classes, strong classification accuracy was achieved. And to maximise performance in the field, I chose to keep the class distribution consistent.

As an aside, I experimented with addressing the class imbalance by weighting the loss function proportionately by using the "class_weight" argument for the "fit_generator" Keras function. (*This is one such method for balancing other classes with the negative class). This resulted in slightly less accurate recall of the Negative class and slightly better classification of the weed classes, with a negligible net accuracy. However when deployed in the field, the original imbalanced model performed much better. This confirmed my heuristic that for best in-field performance, your dataset should exhibit the same class distribution as seen in the field.

from deepweeds.