Instructions to use timm/convnext_large_mlp.clip_laion2b_ft_soup_320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_large_mlp.clip_laion2b_ft_soup_320 with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_large_mlp.clip_laion2b_ft_soup_320", pretrained=True) - Transformers
How to use timm/convnext_large_mlp.clip_laion2b_ft_soup_320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnext_large_mlp.clip_laion2b_ft_soup_320")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/convnext_large_mlp.clip_laion2b_ft_soup_320", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model card for convnext_large_mlp.clip_laion2b_ft_soup_320
timm CLIP (image encoder only) weights from https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup
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