I followed the tutorial to finetune an object-detection network.
My images are initially 2048x2048 and are rescaled to 512x512 during training (function get_dataloader
with data_shape
=512) , and also for detection (using gcv.data.transforms.presets.ssd.load_test
with parameter short
=512 and max_size
=1024).
I can use my fine-tuned network to detect my objects in new 2048x2048 squared images.
I tried then run the detection on cropped images (1466x442) but then it completely fails !
The load_test
function returns an image of dimensions 1024x309 with those rectangular cropped images.
I though the data augmentation used during the training would make the trained network scale-invariant to some extent, or at least such that it stills perform well on cropped images.