I have been working with the Tensorflow Object detection API + Faster R-CNN to detect dead trees from large aerial/satellite images. The images are huge, so they are split up in a 600X600 moving window. The training dataset is not very large (2000 images), so I use transfer learning as descirbed in the API docs to train the last layer of the model which works quite well.
Since I have started the #part1-v2 fast.ai course I was wondering if all I am doing with tensorflow can´t be faster and easier when using fast.ai+pytorch. Also the neat features of the fast.ai library like the learning rate finder, Stochastic Gradient Descent (SGD) with Restart and so on make it very appealing to try this approach! Unfortunately I haven´t found any helpful info on this subject or the pytorch forum about this…
So my question to the people of this lovely forum is, if anybody has tried already to do object detection with the fast.ai library using pretrained pytorch Faster R-CNN, R-FCN, SDD models or could point me in a good direction where to start? @jeremy or is this going to be coverd in #part2 by coincidence?
I have found an two interesting pytorch implementation of Faster R-CNN and for SDD that could be useful for this:
I am looking forward to your responses