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 5.11 kB
-
https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/training_args.bin
- Command line
-
hf download hf://mayank0621/owlvit-base-patch32_FT_cppe5@ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/training_args.bin
5.11 kB
- Xet hash:
- e2a589f4509d16a7a470f5d24da73b8c60f35f0837d982924f55a38220268deb
- Size of remote file:
- 5.11 kB
- SHA256:
- f2db5c94400e745d259df35b8510dec805e0808be4cbdfd342ee3d65d6586864
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