Instructions to use Balajim57/zero-shot-vitb32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Balajim57/zero-shot-vitb32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Balajim57/zero-shot-vitb32") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Balajim57/zero-shot-vitb32") model = AutoModelForZeroShotImageClassification.from_pretrained("Balajim57/zero-shot-vitb32") - Notebooks
- Google Colab
- Kaggle
Upload ViT-B-32.pt
Browse files- ViT-B-32.pt +3 -0
ViT-B-32.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af
|
| 3 |
+
size 353976522
|