Pvt Medium 224

🚀 Pyramid Vision Transformer (medium-sized model)

A pre - trained and fine - tuned vision transformer model for image classification, introduced in a significant research paper.

🚀 Quick Start

The Pyramid Vision Transformer (PVT) is a pre - trained model on ImageNet - 1K and fine - tuned on ImageNet 2012 at a resolution of 224x224. It can be used for image classification tasks.

Here is a simple example of using this model to classify an image:

from transformers import PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-medium-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-medium-224')

inputs = processor(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])

For more code examples, refer to the documentation.

✨ Features

• Progressive Shrinking Pyramid: Unlike ViT models, PVT uses a progressive shrinking pyramid to reduce computations of large feature maps at each stage.
• Versatile for Classification: By adding a [CLS] token at the beginning of the sequence, it can be effectively used for classification tasks.
• Pre - trained Representation: Through pre - training on ImageNet - 1K, it can learn an inner representation of images for downstream tasks.

📚 Documentation

Model description

The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT - like) pretrained on ImageNet - 1k (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 variable - size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. 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 - 1k](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.

Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).

BibTeX entry and citation info

@inproceedings{wang2021pyramid,
  title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng - Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={568--578},
  year={2021}
}

📄 License

This model is licensed under the Apache - 2.0 license.

Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by Rinat S. [@Xrenya].