Hi.
I am trying to apply data augmentation for my classifier and I’ve decided to try out the get_transforms() method from vision.transform. This is the full thing:
get_transforms
get_transforms(do_flip:bool=True,flip_vert:bool=False,max_rotate:float=10.0,max_zoom:float=1.1,max_lighting:float=0.2,max_warp:float=0.2,p_affine:float=0.75,p_lighting:float=0.75,xtra_tfms:Optional[Collection[Transform]]=None) →Collection[Transform]
And this is just an example of how to use it:
tfms = get_transforms(max_rotate=25)
How would I apply tfms to DataBunch? Do I just insert it as a parameter? What is the best way? This is my DataBunch at the moment:
data = ImageList.from_folder(path).split_by_rand_pct(valid_pct=0.2).label_from_re(pat=file_parse).transform(size=224).databunch()
And I’m feeding it into this cnn learner:
top_1 = partial(top_k_accuracy, k=1)
learn = cnn_learner(data, models.resnet50, metrics=[accuracy, top_1], loss_func = LabelSmoothingCrossEntropy(), callback_fns=ShowGraph)
And my other question is this: What do you guys think about a combination of visition.transform + TTA? I am currently running this model on Colab and 5 epochs take plenty of time so I can’t really increase my epochs.