Instructions to use timm/vit_base_patch32_clip_256.datacompxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch32_clip_256.datacompxl with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch32_clip_256.datacompxl", pretrained=True) - Transformers
How to use timm/vit_base_patch32_clip_256.datacompxl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch32_clip_256.datacompxl")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch32_clip_256.datacompxl", dtype="auto") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("timm/vit_base_patch32_clip_256.datacompxl", dtype="auto")Quick Links
Model card for vit_base_patch32_clip_256.datacompxl
timm CLIP (image encoder only) weights from https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch32_clip_256.datacompxl")