dddzg/up-detr
[TPAMI 2022 & CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
This project helps computer vision practitioners train models to automatically identify and locate objects within images, even when they have limited labeled data. You provide raw image datasets, and the system learns general object recognition capabilities without needing extensive manual annotations. The output is a highly effective object detection model that can then be fine-tuned for specific tasks.
489 stars. No commits in the last 6 months.
Use this if you need to build object detection models but want to reduce the dependency on large, costly, human-annotated datasets for initial training.
Not ideal if you require object detection for highly specialized or obscure objects that are significantly different from general real-world objects, or if you don't have access to substantial unlabeled image data.
Stars
489
Forks
72
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 19, 2023
Commits (30d)
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