CVMI-Lab/SyncOOD
(ECCV 2024) Can OOD Object Detectors Learn from Foundation Models?
This project helps computer vision engineers and researchers create synthetic images containing new, unusual objects with bounding box annotations. It takes existing image datasets and a foundation model, then automatically generates diverse scene-level images with novel objects. The output is a dataset of synthetic out-of-distribution (OOD) images that can be used to train robust object detection models.
No commits in the last 6 months.
Use this if you need to train an object detection model to recognize unexpected or novel objects, but lack real-world examples of these 'out-of-distribution' items.
Not ideal if your primary goal is standard in-distribution object detection where all possible object classes are well-represented in your training data.
Stars
24
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 07, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/CVMI-Lab/SyncOOD"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
isl-org/Open3D
Open3D: A Modern Library for 3D Data Processing
cvg/Hierarchical-Localization
Visual localization made easy with hloc
gmberton/CosPlace
Official code for CVPR 2022 paper "Rethinking Visual Geo-localization for Large-Scale Applications"
Vincentqyw/image-matching-webui
🤗 image matching webui
cvg/glue-factory
Training library for local feature detection and matching