Hi everyone,
I’m trying to train a torchvision faster RCNN object detection model using transfer learning.
My dataset consists of tree species from overhead imagery; therefore it is widely different from the coco dataset on which the pretrained models are trained.
I am looking here for recommendations/advices about the transfer learning strategy to adopt.
For my first attempts (on a relatively small dataset) I tried freezing the backbone and training only head + rpn, but the results are not very good.
Should I instead make all layers trainable from start, or train different components separately (e.g.: first only the head, then only the rpn and finally head + rpn + backbone with fpn).
Thanks for any advice,
Loïc