OpenCLIP (LAION-2B)
Collection
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# Load model directly
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
processor = AutoProcessor.from_pretrained("cs-giung/clip-vit-base-patch32-laion2b")
model = AutoModelForZeroShotImageClassification.from_pretrained("cs-giung/clip-vit-base-patch32-laion2b", device_map="auto")Quick Links
CLIP
Contrastive Language-Image Pretraining (CLIP) model pre-trained on LAION-2B at resolution 224x224. It was introduced in the paper Learning Transferable Visual Models From Natural Language Supervision and further reproduced in the follow-up paper Reproducible scaling laws for contrastive language-image learning.
The weights were converted from the laion/CLIP-ViT-B-32-laion2B-s34B-b79K presented in the OpenCLIP LAION-2B collections.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cs-giung/clip-vit-base-patch32-laion2b") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )