Instructions to use natihash/vit_base_patch16_clip_224.text_lp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use natihash/vit_base_patch16_clip_224.text_lp with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.text_lp", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.text_lp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="natihash/vit_base_patch16_clip_224.text_lp") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("natihash/vit_base_patch16_clip_224.text_lp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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tags:
- image-classification
- timm
- transformers
pipeline_tag: image-classification
library_name: timm
license: apache-2.0
---
# Model card for vit_base_patch16_clip_224.text_lp
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