Instructions to use apple/DFN-public with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/DFN-public with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="apple/DFN-public") 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("apple/DFN-public") model = AutoModelForZeroShotImageClassification.from_pretrained("apple/DFN-public") - Notebooks
- Google Colab
- Kaggle
Remove outdated license fields from metadata
Browse files
README.md
CHANGED
|
@@ -1,7 +1,5 @@
|
|
| 1 |
---
|
| 2 |
license: apple-amlr
|
| 3 |
-
license_name: apple-sample-code-license
|
| 4 |
-
license_link: LICENSE
|
| 5 |
---
|
| 6 |
|
| 7 |
A CLIP (Contrastive Language-Image Pre-training) ViT-B/32 model trained on Conceptual Captions 12M, Conceptual Captions 3M, and Shutterstock 15M.
|
|
|
|
| 1 |
---
|
| 2 |
license: apple-amlr
|
|
|
|
|
|
|
| 3 |
---
|
| 4 |
|
| 5 |
A CLIP (Contrastive Language-Image Pre-training) ViT-B/32 model trained on Conceptual Captions 12M, Conceptual Captions 3M, and Shutterstock 15M.
|