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@ -28,7 +28,7 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
* 1k trained variants: `tf_efficientnetv2_s/m/l` * 1k trained variants: `tf_efficientnetv2_s/m/l`
* 21k trained variants: `tf_efficientnetv2_s/m/l_21k` * 21k trained variants: `tf_efficientnetv2_s/m/l_21k`
* 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k` * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k`
* v2 models w/ v1 scaling: `tf_efficientnet_v2_b0` through `b3` * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3`
* Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s` * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s`
* Some blank `efficientnetv2_*` models in-place for future native PyTorch training * Some blank `efficientnetv2_*` models in-place for future native PyTorch training