Image Classification
Transformers
Safetensors
siglip_vision_model
Generated from Trainer
siglip
custom_code
Instructions to use p1atdev/siglip-tagger-test-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/siglip-tagger-test-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="p1atdev/siglip-tagger-test-3", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("p1atdev/siglip-tagger-test-3", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
#2
by not-lain - opened
No description provided.
you can use it using the following code :
from transformers import pipeline
pipe = pipeline("image-classification",model="p1atdev/siglip-tagger-test-3",revision="refs/pr/2",trust_remote_code=True)
pipe("download.jpg",
threshold=0.2, #optional parameter defaults to 0
return_scores = False #optional parameter defaults to False
)
after merging you can get rid of the revision parameter
p1atdev changed pull request status to merged