Instructions to use crossprism/efficientnetv2-21k-fv-m-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use crossprism/efficientnetv2-21k-fv-m-tf with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("crossprism/efficientnetv2-21k-fv-m-tf") - Notebooks
- Google Colab
- Kaggle
EfficientnetV2 M model trained on ImageNet21k, input is a 480x480 image normalized to 0-1.0. Outputs a feature vector of size 1280.
Copied from https://www.kaggle.com/models/google/efficientnet-v2/tensorFlow2/imagenet21k-m-feature-vector
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