ReasonCLIP Models
Collection
Visually Grounded Commonsense Reasoning Supervision for CLIP (ECCV 2026) • 25 items • Updated • 1
How to use RISys-Lab/ReasonCLIP-B32-S1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("zero-shot-image-classification", model="RISys-Lab/ReasonCLIP-B32-S1")
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("RISys-Lab/ReasonCLIP-B32-S1")
model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonCLIP-B32-S1")from transformers import CLIPModel, CLIPProcessor
model_id = "RISys-Lab/ReasonCLIP-B32-S1"
model = CLIPModel.from_pretrained(model_id)
processor = CLIPProcessor.from_pretrained(model_id)
For the full checkpoint list, see the ReasonCLIP model card.