Instructions to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_s 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_s with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-regression-tf_efficientnetv2_s", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5437de1890d57a3cc9e9c5bc32bba9f868f0cebfde4bac3fdabb7f5cd2e250a7
- Size of remote file:
- 81.6 MB
- SHA256:
- 7ecd335228e5b30f21e563d8b4682db0ff21f58edd5a372d089594f7ccfca5ab
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