Instructions to use google/owlv2-base-patch16-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlv2-base-patch16-ensemble with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="google/owlv2-base-patch16-ensemble")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/owlv2-base-patch16-ensemble: direct link, hf CLI and curl.
- Browser
- Download file 620 MB
-
https://huggingface.co/google/owlv2-base-patch16-ensemble/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/owlv2-base-patch16-ensemble/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/owlv2-base-patch16-ensemble/resolve/main/pytorch_model.bin
620 MB
- Xet hash:
- 50f135e2afab367d347bbfff28630ebe5cea3ff3dd8de0b86d376e248e9311af
- Size of remote file:
- 620 MB
- SHA256:
- 69feda8b53b1c9e2a85ae756bf58c120c3c1b4b4a4d97d4876578c1809a63d76
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