Instructions to use hf-internal-testing/tiny-random-data2vec_vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-data2vec_vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-data2vec_vision")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-data2vec_vision") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-data2vec_vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": 30, | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "BeitFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "reduce_labels": false, | |
| "resample": 3, | |
| "size": 30 | |
| } | |