Instructions to use google/owlv2-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlv2-base-patch16 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlv2-base-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from google/owlv2-base-patch16: direct link, hf CLI and curl.
- Browser
- Download file 425 Bytes
-
https://huggingface.co/google/owlv2-base-patch16/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://google/owlv2-base-patch16@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/owlv2-base-patch16/resolve/refs%2Fpr%2F2/preprocessor_config.json
425 Bytes
| { | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Owlv2ImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "Owlv2Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 960, | |
| "width": 960 | |
| } | |
| } | |