Instructions to use kenobi/SDO_VT1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenobi/SDO_VT1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kenobi/SDO_VT1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("kenobi/SDO_VT1") model = AutoModelForImageClassification.from_pretrained("kenobi/SDO_VT1") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesupdated affiliation
README.md
CHANGED
|
@@ -20,7 +20,7 @@ model-index:
|
|
| 20 |
# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
|
| 21 |
|
| 22 |
## Authors:
|
| 23 |
-
[Frank Soboczenski](https://h21k.github.io/), King's College London,
|
| 24 |
[Paul Wright](https://www.wrightai.com/), Wright AI Ltd, Leeds, UK
|
| 25 |
|
| 26 |
## General:
|
|
|
|
| 20 |
# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
|
| 21 |
|
| 22 |
## Authors:
|
| 23 |
+
[Frank Soboczenski](https://h21k.github.io/), University of York & King's College London, UK<br>
|
| 24 |
[Paul Wright](https://www.wrightai.com/), Wright AI Ltd, Leeds, UK
|
| 25 |
|
| 26 |
## General:
|