Instructions to use hf-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vit") 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("hf-internal-testing/tiny-random-vit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vit") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding TF 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:f335ce3ad15e6d1aa6cae1f86d2ec579373286b1e4a40b92c51751355b6d6d95
|
| 3 |
+
size 292392
|