Instructions to use google/siglip2-so400m-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-so400m-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-so400m-patch16-naflex") 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("google/siglip2-so400m-patch16-naflex") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip2-so400m-patch16-naflex") - Notebooks
- Google Colab
- Kaggle
QORA-Vision (Image) - Native Rust Image Encoder based on SigLIP 2
#7 opened 2 months ago
by
drdraq
Inconsistent Results When Passing Batch vs Single
#6 opened 2 months ago
by
ysdk
Not getting great semantic search results with this model
#5 opened 8 months ago
by
stephenmarsh
A question about the training processes for the naflex versions v.s. fixed-resolution versions
1
#4 opened about 1 year ago
by
qijimrc
What is the max image resolution?
7
#2 opened about 1 year ago
by
Alejandro98