Instructions to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_l with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
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) - Transformers
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") - Notebooks
- Google Colab
- Kaggle