feat: ✨ D-FINE Object Detection model inference and training added#348
Open
onuralpszr wants to merge 1 commit intoroboflow:mainfrom
Open
feat: ✨ D-FINE Object Detection model inference and training added#348onuralpszr wants to merge 1 commit intoroboflow:mainfrom
onuralpszr wants to merge 1 commit intoroboflow:mainfrom
Conversation
Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Description
How to Train D-Fine Object Detection on a Custom Dataset notebook added
D-FINE is a powerful real-time object detector that redefines the bounding box regression task in DETRs as Fine-grained Distribution Refinement (FDR) and introduces Global Optimal Localization Self-Distillation (GO-LSD), achieving outstanding performance without introducing additional inference and training costs.
D-FINE is available in 5 different sizes, ranging from
4Mto62Mparameters, and capable of achieving from42.8to55.8mAP on the COCO dataset. It is also available in Object365+COCO trained 4 different sizes, ranging from10Mto62Mparameters, and capable of achieving from50.7to59.3mAP on the Object365 finetuned models