Instructions to use mayank0621/owlvit-base-patch32_FT_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayank0621/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="mayank0621/owlvit-base-patch32_FT_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("mayank0621/owlvit-base-patch32_FT_cppe5") model = AutoModelForZeroShotObjectDetection.from_pretrained("mayank0621/owlvit-base-patch32_FT_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mayank0621/owlvit-base-patch32_FT_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 4.67 kB
-
https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://mayank0621/owlvit-base-patch32_FT_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/main/training_args.bin
4.67 kB
- Xet hash:
- 48aba559152a6d6bbfbd86104a0854dc9024d7433475012483694bfd92edd6fc
- Size of remote file:
- 4.67 kB
- SHA256:
- 55cdb299dd410e1b5d07e3adc51c117258f97026ddb46624c41eeb837d31aff1
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