Instructions to use hf-tiny-model-private/tiny-random-OwlViTForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/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-tiny-model-private/tiny-random-OwlViTForObjectDetection")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OwlViTForObjectDetection") model = AutoModelForZeroShotObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-OwlViTForObjectDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-OwlViTForObjectDetection: direct link, hf CLI and curl.
- Browser
- Download file 1.63 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-OwlViTForObjectDetection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-OwlViTForObjectDetection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-OwlViTForObjectDetection/resolve/main/pytorch_model.bin
1.63 MB
- Xet hash:
- 63cd15b450322793f7239782a276f2cf6585600bf6fdc3120a3d9557fe35d2b4
- Size of remote file:
- 1.63 MB
- SHA256:
- 6f0b6f5353347fb28c5e0b4d1098c3c0df1d83813ba5b2747eaaa2afaf2ac68e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.