How to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_l with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-regression-tf_efficientnetv2_l", pretrained=True)
How to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_l with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mwalmsley/baseline-encoder-regression-tf_efficientnetv2_l") 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-tf_efficientnetv2_l", dtype="auto")
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