BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in
this repository.
1from mlx_ssl.models import Data2VecVisionForImageClassification
2
3model = Data2VecVisionForImageClassification.from_pretrained(
4 "mlx-community/data2vec-vision-large-ft1k"
5)
