Emerging Properties in Self-Supervised Vision Transformers
Paper • 2104.14294 • Published • 4
How to use probing-vits/vit-dino-base16 with Keras:
# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
model = keras.saving.load_model("hf://probing-vits/vit-dino-base16")
This model is a TensorFlow port of DINO [1] ViT B-16 [2]. The backbone of this model was pre-trained using the DINO pretext task. After that its head layer was trained by keeping the backbone frozen. ImageNet-1k dataset was used for training purposes. You can refer to this notebook to know how the porting was done.
[1] Emerging Properties in Self-Supervised Vision Transformers: https://arxiv.org/abs/2104.14294
[2] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929