Instructions to use Kaspar/vit-base-railspace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaspar/vit-base-railspace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kaspar/vit-base-railspace") 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("Kaspar/vit-base-railspace") model = AutoModelForImageClassification.from_pretrained("Kaspar/vit-base-railspace") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -41,10 +41,10 @@ More information needed
|
|
| 41 |
1 0.92 0.94 0.93 204
|
| 42 |
2 0.95 0.97 0.96 714
|
| 43 |
3 0.87 0.98 0.92 171
|
| 44 |
-
|
| 45 |
-
accuracy 0.99 12404
|
| 46 |
macro avg 0.93 0.97 0.95 12404
|
| 47 |
weighted avg 0.99 0.99 0.99 12404
|
|
|
|
|
|
|
| 48 |
|
| 49 |
|
| 50 |
## Training procedure
|
|
|
|
| 41 |
1 0.92 0.94 0.93 204
|
| 42 |
2 0.95 0.97 0.96 714
|
| 43 |
3 0.87 0.98 0.92 171
|
|
|
|
|
|
|
| 44 |
macro avg 0.93 0.97 0.95 12404
|
| 45 |
weighted avg 0.99 0.99 0.99 12404
|
| 46 |
+
accuracy 0.99 12404
|
| 47 |
+
|
| 48 |
|
| 49 |
|
| 50 |
## Training procedure
|