Internal Imagenet normalisation for pretrained squeezenet models #785
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Makes it easier to normalise the model weights with imagenet mean and std when using transfer learning. Useful for beginners who forget to normalise the input images while using transfer learning.
Codecov Report
@@ Coverage Diff @@
## master #785 +/- ##
==========================================
- Coverage 38.13% 38.05% -0.08%
==========================================
Files 32 32
Lines 3126 3132 +6
Branches 487 488 +1
==========================================
Hits 1192 1192
- Misses 1855 1861 +6
Partials 79 79
Continue to review full report at Codecov.
|
|
Same comment from #782 (comment) applies |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Makes it easier to normalise the model weights with imagenet mean and std when using transfer learning. Useful for beginners who forget to normalise the input images while using transfer learning.