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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- vision |
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datasets: |
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- imagenet |
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- imagenet-21k |
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--- |
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# Vision Transformer (large-sized model) |
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Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. |
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Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384. |
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Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), 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. |
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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. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import ViTFeatureExtractor, ViTForImageClassification |
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from PIL import Image |
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import requests |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch32-384') |
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model = ViTForImageClassification.from_pretrained('google/vit-large-patch32-384') |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. |
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## Training data |
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The ViT 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. |
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## Training procedure |
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### Preprocessing |
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The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). |
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Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). |
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### Pretraining |
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The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224. |
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## Evaluation results |
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For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 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. |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{wu2020visual, |
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title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, |
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author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, |
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year={2020}, |
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eprint={2006.03677}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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```bibtex |
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@inproceedings{deng2009imagenet, |
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title={Imagenet: A large-scale hierarchical image database}, |
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author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, |
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booktitle={2009 IEEE conference on computer vision and pattern recognition}, |
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pages={248--255}, |
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year={2009}, |
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organization={Ieee} |
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} |
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``` |
