Instructions to use google/owlvit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlvit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="google/owlvit-base-patch32")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlvit-base-patch32") - Notebooks
- Google Colab
- Kaggle
HuggingFace integration reference image mode works significantly worse than original repository
#6
by maxtes - opened
Hi! Your integration works fine in text prompt mode, but when it comes to reference image mode, model's predictions look inadequate in comparison to predictions of original model on the same image with the same settings. You can check predictions of original model here.
I will attach an image for comparison to this comment.
yeah I noticed the same.
Any updates on this?
any updates?

