Instructions to use natihash/vit_base_patch16_clip_224.laion2b_linear_probe 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.laion2b_linear_probe with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.laion2b_linear_probe", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.laion2b_linear_probe 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.laion2b_linear_probe") 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.laion2b_linear_probe", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 413dce5ed7909f2254700daaa034b3b4dc2698b106a5a1b002e7542b5988430f
- Size of remote file:
- 346 MB
- SHA256:
- 6dc6096096d61daeef291b78cea4479e4c3a2853b27848b55d7cbf6c307d3ee6
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