Instructions to use Thamer/vit-base-fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thamer/vit-base-fine_tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thamer/vit-base-fine_tuned") 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("Thamer/vit-base-fine_tuned") model = AutoModelForImageClassification.from_pretrained("Thamer/vit-base-fine_tuned") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:92d2b735eff3640ddaabec9d2daae29ec24668d8d82764f60cac168f56848df5
|
| 3 |
+
size 350148288
|