ViT (ImageNet-21k)
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# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("cs-giung/vit-small-patch16-imagenet21k-augreg")
model = AutoModelForImageClassification.from_pretrained("cs-giung/vit-small-patch16-imagenet21k-augreg", device_map="auto")Quick Links
Vision Transformer
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale and further enhanced in the follow-up paper How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.
The weights were converted from the S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz file in GCS buckets presented in the original repository.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cs-giung/vit-small-patch16-imagenet21k-augreg") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")