Image Classification
Transformers
PyTorch
TensorFlow
data2vec-vision
image-feature-extraction
vision
Instructions to use facebook/data2vec-vision-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-vision-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/data2vec-vision-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/data2vec-vision-base") model = AutoModel.from_pretrained("facebook/data2vec-vision-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from facebook/data2vec-vision-base: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/facebook/data2vec-vision-base/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://facebook/data2vec-vision-base/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/facebook/data2vec-vision-base/resolve/main/preprocessor_config.json
302 Bytes
| { | |
| "crop_size": 224, | |
| "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": 224 | |
| } | |