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
Add TensorFlow weights
Browse files- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:506e344b373f3dc491c4fc7bb4bafeb405c1708ee6038350e16c25a2e2d8d142
|
| 3 |
+
size 342959528
|