Instructions to use rathi2023/owlvit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rathi2023/owlvit-base-patch32 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("rathi2023/owlvit-base-patch32") model = AutoModelForZeroShotObjectDetection.from_pretrained("rathi2023/owlvit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rathi2023/owlvit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/rathi2023/owlvit-base-patch32/resolve/main/training_args.bin
- Command line
-
hf download hf://rathi2023/owlvit-base-patch32/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rathi2023/owlvit-base-patch32/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- be889bf077a1f868b51812ac1a53cb260dad6d4a4184acf2e62a2af6389eb66c
- Size of remote file:
- 4.92 kB
- SHA256:
- 9ba25ec2cce35b14484b16f852534afdc2f37b62e24e09056ae9f9e6ee789d05
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