Instructions to use timm/visformer_tiny.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/visformer_tiny.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/visformer_tiny.in1k", pretrained=True) - Transformers
How to use timm/visformer_tiny.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/visformer_tiny.in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/visformer_tiny.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 374965996d816c23ae7c8e06be327a5ae34d0d9a2d03ef0d6a5654ee64dbb888
- Size of remote file:
- 41.4 MB
- SHA256:
- 1dacc203aa44db20389ebbb0f16b30dd74808bacb26948aa107e7d691d8bab67
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