Instructions to use Prot10/vit-base-patch16-224-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/vit-base-patch16-224-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/vit-base-patch16-224-for-pre_evaluation") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Prot10/vit-base-patch16-224-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/vit-base-patch16-224-for-pre_evaluation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 16
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 343277933
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:428097f3c9bab8a4f9b75b040134b3d205efd39e99226fb93781f34467f11a48
|
| 3 |
size 343277933
|