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 tokenizer.json from mayank0621/owlvit-base-patch32_FT_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/tokenizer.json
- Command line
-
hf download hf://mayank0621/owlvit-base-patch32_FT_cppe5@ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/ee2e2a6d64b3af2ffc7c7b24d5abfbcf62c8cc42/tokenizer.json
2.22 MB
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