I want to train a model with tensorflow faster rcnn that can detect animals from a "far" distance where the objects are relativly small (example: https://cdn1.spiegel.de/images/image-830326-breitwandaufmacher-bfrb-830326.jpg ).
Much of my training data is was captured closer (example: https://www.welt.de/img/reise/nah/mobile139849438/0572506457-ci102l-w1024/Schafe-in-bei-Clifden-irische-Kleinstad.jpg ) How can my model detect smaller objects better that look pretty similar to my training data but are smaller?
I already tried to augment my Data in the pipeline (faster rcnn config). This improved my results lot but its still not perfect.
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_pad_image {
}
}
I also tried to lower the scales of my anchors to detect the smaller objects in my dataset better, but this had a bad impact on the model
I also tried to add the same picture in different aspect ratios to the tfrecords, which had till now a bad impact on the model. What to you think i can change to get better results with my model?