Model Card for rope_deit3_reg4_m14_arabian-peninsula

A RoPE DeiT3 reg4 image classification model. This model was trained on the arabian-peninsula dataset (all the relevant bird species found in the Arabian peninsula inc. rarities).

The species list is derived from data available at https://avibase.bsc-eoc.org/checklist.jsp?region=ARA.

Model Details

• Model Type: Image classification and detection backbone

• Model Stats:

  • Params (M): 38.7
  • Input image size: 252 x 252

• Dataset: arabian-peninsula (735 classes)

• Papers:

  • DeiT III: Revenge of the ViT: https://arxiv.org/abs/2204.07118
  • Rotary Position Embedding for Vision Transformer: https://arxiv.org/abs/2403.13298

Model Usage

Image Classification

import birder
from birder.inference.classification import infer_image

(net, model_info) = birder.load_pretrained_model("rope_deit3_reg4_m14_arabian-peninsula", 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)

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, 735), representing class probabilities.

Image Embeddings

import birder
from birder.inference.classification import infer_image

(net, model_info) = birder.load_pretrained_model("rope_deit3_reg4_m14_arabian-peninsula", 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)

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, 512)

Detection Feature Map

from PIL import Image
import birder

(net, model_info) = birder.load_pretrained_model("rope_deit3_reg4_m14_arabian-peninsula", 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)

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:
# [('neck', torch.Size([1, 512, 18, 18]))]

Citation

@misc{touvron2022deitiiirevengevit,
      title={DeiT III: Revenge of the ViT},
      author={Hugo Touvron and Matthieu Cord and Hervé Jégou},
      year={2022},
      eprint={2204.07118},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2204.07118},
}

@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},
}