Introducing OWLv2: Google's Breakthrough in Zero-Shot Object Detection
Zero-shot object detection is made easy with Google's OWLv2 model.
We provide a step-by-step guide on using Google's OWLv2 model for zero-shot and image-guided object detection. OWLv2 is a powerful model capable of detecting objects in images without the need for manually annotated bounding boxes.
To get started, you need Python and a few libraries installed. You can follow the provided code examples to set up the environment.
Learn how to use OWLv2 for zero-shot object detection, process images, and visualize the results. The article provides code examples and explanations for each step.
Discover how to perform image-guided object detection with OWLv2. Use a single query image to detect objects in new images. The article includes code and instructions.
Feel free to explore the article and leverage OWLv2 for your object detection needs!
Links:
https://github.com/NielsRogge
https://huggingface.co/docs/transformers/main/en/model_doc/owlv2
https://arxiv.org/abs/2306.09683
https://huggingface.co/docs/transformers/main/en/model_doc/owlvit
https://arxiv.org/abs/2205.06230
Minderer, M., Gritsenko, A., & Houlsby, N. (2023). Scaling Open-Vocabulary Object Detection. ArXiv. /abs/2306.09683