Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
contrastive-learning
zero-shot
custom_code
Instructions to use nebulette/tipsv2-b14-vision-module with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nebulette/tipsv2-b14-vision-module with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="nebulette/tipsv2-b14-vision-module", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nebulette/tipsv2-b14-vision-module", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload images.
Browse files- .gitattributes +1 -0
- images/pca.png +3 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
images/pca.png filter=lfs diff=lfs merge=lfs -text
|
images/pca.png
ADDED
|
Git LFS Details
|