Instructions to use mwalmsley/baseline-encoder-regression-maxvit_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/baseline-encoder-regression-maxvit_base with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-regression-maxvit_base", pretrained=True) - Transformers
How to use mwalmsley/baseline-encoder-regression-maxvit_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/baseline-encoder-regression-maxvit_base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mwalmsley/baseline-encoder-regression-maxvit_base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b7470daddef60521a2a648bb1411aab6dea86cf6bca35e869018527e52c619c
- Size of remote file:
- 463 MB
- SHA256:
- b5940033c9dd64b9010e08b26ce7b8f2b6a5dbdb1ac30c178177741374a6460d
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