Hello,
I’m trying to do a binary classification.
I would like to know if there is a way, similar to TTA(), to apply the same transformation I used during training with aug_tfms to new images in the inference stage and get the average classification for the non augmented image.
For exemple if I have :
tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)
learn = load_learner(data_folder, test = ImageItemList.from_folder(data_folder/‘data_to_predict’))
Can I easily apply tfms on test and get the mean classification for each original image ?
Thank you !
The idea is based on this paper : Data Augmentation for Skin Lesion Analysis : https://arxiv.org/pdf/1809.01442.pdf