Instructions to use google/owlvit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlvit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="google/owlvit-base-patch32")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlvit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from google/owlvit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 392 Bytes
-
https://huggingface.co/google/owlvit-base-patch32/resolve/refs%2Fpr%2F9/preprocessor_config.json
- Command line
-
hf download hf://google/owlvit-base-patch32@refs/pr/9/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/owlvit-base-patch32/resolve/refs%2Fpr%2F9/preprocessor_config.json
392 Bytes
| { | |
| "crop_size": 768, | |
| "do_center_crop": false, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "OwlViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "OwlViTProcessor", | |
| "resample": 3, | |
| "size": [768, 768] | |
| } | |