You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Specifically, these are "missile" and "sunglasses" -- both of which appear 2 times in the provided label list. I've gone through and fixed the issues on my end by referencing Anish Athalye's imagenet-simple-labels: one "missile" label should be "projectile" and one "sunglasses" label should be "sunglass".
What I believe is the "correct" label list is here:
I was able to match your reported ViT-B/32 top-1 performance on ImageNet from the paper (63.2) using the "wrong" label set provided by this repo. However, fixing the missing labels yields a better top-1 of 63.47 (so +.27%). I imagine the numbers of the other models may change too.
Best,
George
The text was updated successfully, but these errors were encountered:
Hello!
Apologies if this has already been addressed somewhere else, but I noticed that the ImageNet labels in the "Prompt Engineering for ImageNet" notebook: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb, contained two duplicates.
Specifically, these are "missile" and "sunglasses" -- both of which appear 2 times in the provided label list. I've gone through and fixed the issues on my end by referencing Anish Athalye's imagenet-simple-labels: one "missile" label should be "projectile" and one "sunglasses" label should be "sunglass".
What I believe is the "correct" label list is here:
I was able to match your reported ViT-B/32 top-1 performance on ImageNet from the paper (63.2) using the "wrong" label set provided by this repo. However, fixing the missing labels yields a better top-1 of 63.47 (so +.27%). I imagine the numbers of the other models may change too.
Best,
George
The text was updated successfully, but these errors were encountered: