EntityNet
Collection
The models of our publication: Using Knowledge Graphs to harvest datasets for efficient CLIP model training • 7 items • Updated
How to use lmb-freiburg/CLIP-ViT-B-32-EntityNet-33M-Finetuned with OpenCLIP:
import open_clip
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:lmb-freiburg/CLIP-ViT-B-32-EntityNet-33M-Finetuned')
tokenizer = open_clip.get_tokenizer('hf-hub:lmb-freiburg/CLIP-ViT-B-32-EntityNet-33M-Finetuned')A CLIP (Contrastive Language-Image Pre-training) model finetuned on EntityNet-33M. The base model is ViT-B-32/datacomp_xl_s13b_b90k.
See the project page for the paper, code, usage examples, metrics, etc.
The model has seen ~13B images at a batch size of 90k during pretraining, and ~0.6B images at a batch size of 32k during finetuning.