I am doing semantic segmentation (multi-class classification of image pixels) using convolutional neural networks (CNN) in Keras.
In particular, I am applying this to aerial images of crops (vegetation). In Keras, I successfully developed a workflow to segment/classify different crops for one specific dataset (let's call this dataset rural area #1).
Can I apply my Keras weights trained on rural area #1 for initializing the training of another dataset rural area #2? Such as:
model = load_model("weights_ruralarea1.hdf5")
Then I will proceed to model.fit in Keras.
The rural area #2 dataset has a little amount of training images for training the CNN.
The images in this 2nd dataset, although it has similar crop content on the images to the 1st dataset, it also has different image resolution, and is not exactly the same visually as the 1st dataset.
So would using my weights for rural area #1 be a form of transfer learning? or will it be a form of fine-tuning?
Best Answer
1) If you have little to no data in the second dataset, the best you can do is use the first one. If you have "some" data you backpropogate through the last layer, if you have "some more" data, you back propagate through the last 2 layers. Using a well trained model could be good enough in itself (For instance, ResNet could be used right out the box with no tuning whatsoever
2) For a different size image, I think you could just reshape the image to that in dataset1. See https://keras.io/applications/#usage-examples-for-image-classification-models, for an example where the image is reshaped to 224 x 224 to fit the ResNet50 needs