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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc78d13f72073e7463c0ddc9a25b82c741086b5b90d3e10e499520444a5ccdfb
- Size of remote file:
- 472 MB
- SHA256:
- ee1bea79cb61c0883c861f9de9dbf84e342fa1d1f8e691c83fe16ae90ad8781b
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