Instructions to use timm/convnext_base.clip_laiona_320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_base.clip_laiona_320 with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_base.clip_laiona_320", pretrained=True) - Transformers
How to use timm/convnext_base.clip_laiona_320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnext_base.clip_laiona_320")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/convnext_base.clip_laiona_320", dtype="auto") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("timm/convnext_base.clip_laiona_320", dtype="auto")Quick Links
Model card for convnext_base.clip_laiona_320
timm CLIP (image encoder only) weights from https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
- Downloads last month
- 112
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnext_base.clip_laiona_320")