
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) 

BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). 

Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet", "imagenet-21k"]}
transformers pytorch jax beit image-classification dataset:imagenet dataset:imagenet-21k arxiv:2106.08254 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) 

BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. 

Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) 

BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. 

Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
TAGS
#transformers #pytorch #jax #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) 

BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. 

Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
passage: TAGS
#transformers #pytorch #jax #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) 

BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. 

Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.