rope_vit_reg4_b14_capi-intermediate-eu-common

Model Card for rope_vit_reg4_b14_capi-intermediate-eu-common

A RoPE ViT image classification model. The model follows a three-stage training process: first, CAPI pretraining, next intermediate training on a large-scale dataset containing diverse bird species from around the world, finally fine-tuned specifically on the eu-common dataset.

The species list is derived from the Collins bird guide ¹.

Model Details

• Model Type: Image classification and detection backbone

• Model Stats:

  • Params (M): 86.5
  • Input image size: 336 x 336

• Dataset: eu-common (707 classes)

  • Intermediate training involved ~8000 species around the world

• Papers:

  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929
  • Rotary Position Embedding for Vision Transformer: https://arxiv.org/abs/2403.13298
  • Vision Transformers Need Registers: https://arxiv.org/abs/2309.16588
  • Cluster and Predict Latent Patches for Improved Masked Image Modeling: https://arxiv.org/abs/2502.08769

Model Usage

Image Classification

import birder
from birder.inference.classification import infer_image

# Option 1: manual setup (more control over preprocessing)
(net, model_info) = birder.load_pretrained_model("rope_vit_reg4_b14_capi-intermediate-eu-common", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

# Option 2: helper (quick start with default preprocessing)
(net, model_info, transform) = birder.load_pretrained_model_and_transform("rope_vit_reg4_b14_capi-intermediate-eu-common", inference=True)

image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, 707), representing class probabilities.

Image Embeddings

import birder
from birder.inference.classification import infer_image

# Option 1: manual setup (more control over preprocessing)
(net, model_info) = birder.load_pretrained_model("rope_vit_reg4_b14_capi-intermediate-eu-common", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

# Option 2: helper (quick start with default preprocessing)
(net, model_info, transform) = birder.load_pretrained_model_and_transform("rope_vit_reg4_b14_capi-intermediate-eu-common", inference=True)

image = "path/to/image.jpeg"  # or a PIL image
(out, embedding) = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 768)

Detection Feature Map

from PIL import Image
import birder

(net, model_info, transform) = birder.load_pretrained_model_and_transform("rope_vit_reg4_b14_capi-intermediate-eu-common", inference=True)

image = Image.open("path/to/image.jpeg")
features = net.detection_features(transform(image).unsqueeze(0))
# features is a dict (stage name -> torch.Tensor)
print([(k, v.size()) for k, v in features.items()])
# Output example:
# [('stage1', torch.Size([1, 768, 24, 24]))]

Citation

@misc{dosovitskiy2021imageworth16x16words,
      title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
      author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
      year={2021},
      eprint={2010.11929},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2010.11929},
}

@misc{heo2024rotarypositionembeddingvision,
      title={Rotary Position Embedding for Vision Transformer},
      author={Byeongho Heo and Song Park and Dongyoon Han and Sangdoo Yun},
      year={2024},
      eprint={2403.13298},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2403.13298},
}

@misc{darcet2024visiontransformersneedregisters,
      title={Vision Transformers Need Registers},
      author={Timothée Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
      year={2024},
      eprint={2309.16588},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2309.16588},
}

@misc{darcet2025clusterpredictlatentpatches,
      title={Cluster and Predict Latent Patches for Improved Masked Image Modeling},
      author={Timothée Darcet and Federico Baldassarre and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
      year={2025},
      eprint={2502.08769},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.08769},
}

Footnotes

1. Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. ↩