Vit Large Patch16 384

🚀 Vision Transformer (large-sized model)

Vision Transformer (ViT) is a transformer encoder model pre-trained on a large collection of images, which can be used for image classification tasks.

🚀 Quick Start

The Vision Transformer (ViT) model is pre-trained on ImageNet - 21k (14 million images, 21,843 classes) at a resolution of 224x224 and fine - tuned on ImageNet 2012 (1 million images, 1,000 classes) at a resolution of 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository.

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.

✨ Features

• Powerful Representation Learning: Through pre - training on a large - scale image dataset (ImageNet - 21k), the model can learn rich inner representations of images.
• Fine - Tuning Capability: It can be fine - tuned on specific datasets (e.g., ImageNet 2012) to adapt to different downstream tasks.
• Versatile for Classification: Suitable for image classification tasks, and can be used to classify images into different classes.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import ViTFeatureExtractor, ViTForImageClassification
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 = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-384')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384')
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])

Advanced Usage

Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.

📚 Documentation

Model description

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.

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 to look for fine - tuned versions on a task that interests you.

🔧 Technical Details

Training data

The ViT model was pretrained on ImageNet - 21k, a dataset consisting of 14 million images and 21k classes, and fine - tuned on ImageNet, 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.

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).

Pretraining

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.

Evaluation results

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.

BibTeX entry and citation info

@misc{wu2020visual,
      title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, 
      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},
      year={2020},
      eprint={2006.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@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}
}

📄 License

This project is licensed under the Apache - 2.0 license.