Instructions to use rathi2023/owlvit-base-patch32_FT_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rathi2023/owlvit-base-patch32_FT_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="rathi2023/owlvit-base-patch32_FT_cppe5")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("rathi2023/owlvit-base-patch32_FT_cppe5") model = AutoModelForZeroShotObjectDetection.from_pretrained("rathi2023/owlvit-base-patch32_FT_cppe5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16e48e10c8576576c11cf1de121df849dbda5281614800935916deabfd08abe1
- Size of remote file:
- 4.92 kB
- SHA256:
- 45819f162e43ce319c719e6844b75876ca137dfe5973992361c5df7516799042
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