Vit Base R50 S16 224.orig In21k

🚀 vit_base_r50_s16_224.orig_in21k

A ResNet - Vision Transformer (ViT) hybrid image classification model. Pretrained on ImageNet-21k, useful for feature extraction and fine-tuning.

🚀 Quick Start

This is a ResNet - Vision Transformer (ViT) hybrid image classification model. It was pretrained on ImageNet - 21k by the paper authors in JAX and then ported to PyTorch by Ross Wightman. This model doesn't have a classification head, so it's only useful for feature extraction and fine - tuning.

✨ Features

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 97.9
  • GMACs: 20.9
  • Activations (M): 27.9
  • Image size: 224 x 224
• Papers:
  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Dataset: ImageNet - 21k
• Original: https://github.com/google-research/vision_transformer

📦 Installation

The README doesn't provide installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('vit_base_r50_s16_224.orig_in21k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Advanced Usage

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'vit_base_r50_s16_224.orig_in21k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

📚 Documentation

Explore the dataset and runtime metrics of this model in timm model results.

📄 License

The model is licensed under the Apache - 2.0 license.

📖 Citation

@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}