Add F1 score metric to detection metrics#555
Closed
kayampady wants to merge 1 commit intoJdeRobot:masterfrom
Closed
Add F1 score metric to detection metrics#555kayampady wants to merge 1 commit intoJdeRobot:masterfrom
kayampady wants to merge 1 commit intoJdeRobot:masterfrom
Conversation
3fb45a6 to
e0051ca
Compare
Contributor
Author
|
The failing tests appear unrelated to the F1 metric changes in this PR. All detection and segmentation metric tests pass successfully. The failure is coming from tests/test_lidar.py with an Open3D-related TypeError in CI: This seems to be an environment/dependency issue with Open3D. Related issue: #556 Happy to help debug or take this up in a separate PR if needed. |
Contributor
Author
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
This PR adds F1 score as an evaluation metric for detection tasks.
F1 score provides a balanced measure of precision and recall, complementing the existing metrics in the detection pipeline.
The implementation integrates with the current metric computation framework and follows the existing design patterns.