I am trying to get feature vectors of images from models pre-trained on imagenet. I am using
learn.get_preds() since it does the scoring in batches which is much quicker than scoring individually.
The problem I am having is I can’t seem to confirm that rows in my predictions tensor (number of images x 1000 (output size of pre-trained models)). I am trying to compare the model output of the first input in my dataset with the output of the
get_preds, but the predictions are different. I tried to check if it was because of fastai automatically normalizing on
get_preds, but may have not checked that correctly. Any help would be appreciated.
Below is code I am using and a screen shot of different vector outputs.
# fastai 1.0.52 (SageMaker notebook) data = ( ImageList .from_folder(path/'product-images/square-224') .split_none() .label_empty() .databunch(bs=16) .normalize(imagenet_stats)) data_test = ( ImageList .from_folder(path/'product-images/square-224')) learn = Learner(data, m) preds = learn.get_preds('train')