Instructions to use fxmarty/owlvit-tiny-non-contiguous-weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fxmarty/owlvit-tiny-non-contiguous-weight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="fxmarty/owlvit-tiny-non-contiguous-weight")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("fxmarty/owlvit-tiny-non-contiguous-weight") model = AutoModelForZeroShotObjectDetection.from_pretrained("fxmarty/owlvit-tiny-non-contiguous-weight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab17b2b4ad30b7e52675dde9ad16f8ed0734996186515d76563f5c5243d6a4d2
- Size of remote file:
- 1.63 MB
- SHA256:
- bb24c3ab43fbb9abab87386fbcbeedc50da7e53cd176b7f52df5de96d426754b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.