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 preprocessor_config.json from hf-internal-testing/tiny-random-OwlViTForObjectDetection: direct link, hf CLI and curl.
- Browser
- Download file 317 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-OwlViTForObjectDetection/resolve/44f18127f2ecd315c2dc82903ab094e507741e4f/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-OwlViTForObjectDetection@44f18127f2ecd315c2dc82903ab094e507741e4f/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-OwlViTForObjectDetection/resolve/44f18127f2ecd315c2dc82903ab094e507741e4f/preprocessor_config.json
317 Bytes
| { | |
| "crop_size": 32, | |
| "do_center_crop": false, | |
| "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 | |
| ], | |
| "resample": 3, | |
| "size": 32 | |
| } | |