Instructions to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_m 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_m with timm:
import timm model = timm.create_model("hf_hub:mwalmsley/baseline-encoder-regression-tf_efficientnetv2_m", pretrained=True) - Transformers
How to use mwalmsley/baseline-encoder-regression-tf_efficientnetv2_m 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_m") 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_m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d652ea6904c5143f91c25d3091b297a7411aa32c730cc8d5ecbccc4c651a690
- Size of remote file:
- 213 MB
- SHA256:
- 4b4347879db23f8a9a6f2fac287863b2ea2808af037afbbd7477f4ce9237b9c7
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