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") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 302 Bytes
cc3a0b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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
}
|