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
- Xet hash:
- 6eaf07b582635f06520710981ec29c2cc5c7a04b582f54f71624b5044f7e78d8
- Size of remote file:
- 613 MB
- SHA256:
- 4dbe0399f0b7d7c8dddf1535a98769cc30743bebc877aea681998c8d984ce52b
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