Instructions to use hf-internal-testing/tiny-random-OwlViTForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-OwlViTForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="hf-internal-testing/tiny-random-OwlViTForObjectDetection")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-OwlViTForObjectDetection") model = AutoModelForZeroShotObjectDetection.from_pretrained("hf-internal-testing/tiny-random-OwlViTForObjectDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-OwlViTForObjectDetection: direct link, hf CLI and curl.
- Browser
- Download file 1.63 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-OwlViTForObjectDetection/resolve/44f18127f2ecd315c2dc82903ab094e507741e4f/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-OwlViTForObjectDetection@44f18127f2ecd315c2dc82903ab094e507741e4f/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-OwlViTForObjectDetection/resolve/44f18127f2ecd315c2dc82903ab094e507741e4f/pytorch_model.bin
1.63 MB
- Xet hash:
- a42a9b034dee80384a5dd7a11066ff1414d0ab4662785c879010a666eb15c7b7
- Size of remote file:
- 1.63 MB
- SHA256:
- 0cbcd371b15de2ee270fa71cbfcc4c1af17580e0219b0c07c8c07538dab19b1b
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