Instructions to use WinKawaks/vit-tiny-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WinKawaks/vit-tiny-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="WinKawaks/vit-tiny-patch16-224") 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("WinKawaks/vit-tiny-patch16-224") model = AutoModelForImageClassification.from_pretrained("WinKawaks/vit-tiny-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,9 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- vision
|
| 5 |
+
- image-classification
|
| 6 |
+
datasets:
|
| 7 |
+
- imagenet
|
| 8 |
+
|
| 9 |
+
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository] (https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base] (https://huggingface.co/google/vit-base-patch16-224).
|