Instructions to use dleemiller/siglip2-math-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dleemiller/siglip2-math-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="dleemiller/siglip2-math-base-patch16-256") 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("dleemiller/siglip2-math-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("dleemiller/siglip2-math-base-patch16-256") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- shiwk24/MathCanvas-Imagen
base_model:
- google/siglip2-base-patch16-224
library_name: transformers
SigLip2 Math
This version of siglip2 is fine tuned on shiwk24/MathCanvas-Imagen using the code_derived_captions split.
I trained for 1 epoch on 4M math images, with a random selection between the tikz code or caption using a batch size of 640.
This is not a classification model, since the loss function was pairwise contrastive loss. Use for embedding or downstream classifier training is recommended.
