Instructions to use timm/convit_base.fb_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convit_base.fb_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convit_base.fb_in1k", pretrained=True) - Transformers
How to use timm/convit_base.fb_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convit_base.fb_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/convit_base.fb_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78e8810eaca1716de542a98c315f25baa87236c3534a817b700d720b2b67f3ae
- Size of remote file:
- 346 MB
- SHA256:
- 0dea1341b12a3f502517f99e08924a1c2b3169e5b816cc0def8965246afceb5b
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