Instructions to use timm/vgg16.tv_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vgg16.tv_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vgg16.tv_in1k", pretrained=True) - Transformers
How to use timm/vgg16.tv_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vgg16.tv_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/vgg16.tv_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f46181458222acae5398de6035759088721b9df072208cbeab6631671c9dda26
- Size of remote file:
- 553 MB
- SHA256:
- 57b026918159a6bf9faf8405c3a551903768e7138989d9c6224a14227203fad8
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