Instructions to use mwalmsley/baseline-encoder-classification-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-classification-tf_efficientnetv2_s with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-classification-tf_efficientnetv2_s", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac02e43398c9a11a7df1f883a8eefde7f099cb55ed06ae8118bf3d171e4c4339
- Size of remote file:
- 81.6 MB
- SHA256:
- a54230da89f826e24ea9f31d537cfdc5a0b966d2091a9597b742ab738da9f983
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