Instructions to use google/siglip2-so400m-patch14-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-so400m-patch14-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-so400m-patch14-384") 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("google/siglip2-so400m-patch14-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Difference in architecture between the inference-time model and the implementation in the transformers code
#7 opened 5 months ago
by
DavidNguyen
Adding ONNX file of this model
#6 opened 9 months ago
by
pySilver
get low ImageNet1k zero-shot classification accuracy
👍 1
#5 opened 10 months ago
by
BillionZheng
Fix model_max_length
2
#4 opened about 1 year ago
by
fancyfeast