Add model card

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+datasets:
+- imagenet
+- imagenet-21k
+---
+
+# BEiT (base-sized model) 
+
+BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. 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.
+
+## Model description
+
+The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches.
+Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
+
+Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
+
+By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=microsoft/beit) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+from transformers import BEiTFeatureExtractor, BEiTForImageClassification
+from PIL import Image
+import requests
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+feature_extractor = BEiTFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224')
+model = BEiTForImageClassification.from_pretrained('google/beit-base-patch16-224')
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+# model predicts one of the 1000 ImageNet classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+
+Currently, both the feature extractor and model support PyTorch.
+
+## Training data
+
+The BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. 
+
+## Training procedure
+
+### Preprocessing
+
+The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). 
+
+Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+### Pretraining
+
+For all pre-training related hyperparameters, we refer to page 15 of the [original paper](https://arxiv.org/abs/2106.08254).
+
+## Evaluation results
+
+For evaluation results on several image classification benchmarks, we refer to tables 1 and 2 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
+
+### BibTeX entry and citation info
+
+```@article{DBLP:journals/corr/abs-2106-08254,
+  author    = {Hangbo Bao and
+               Li Dong and
+               Furu Wei},
+  title     = {BEiT: {BERT} Pre-Training of Image Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2106.08254},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2106.08254},
+  archivePrefix = {arXiv},
+  eprint    = {2106.08254},
+  timestamp = {Tue, 29 Jun 2021 16:55:04 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08254.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+```bibtex
+@inproceedings{deng2009imagenet,
+  title={Imagenet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  booktitle={2009 IEEE conference on computer vision and pattern recognition},
+  pages={248--255},
+  year={2009},
+  organization={Ieee}
+}
+```