Model Card for convnext_v2_tiny_intermediate-eu-common

A ConvNext v2 image classification model. The model follows a two-stage training process: first undergoing intermediate training on a large-scale dataset containing diverse bird species from around the world, then fine-tuned specifically on the eu-common dataset containing common European bird species.

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

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

Model Details

• Model Type: Image classification and detection backbone

• Model Stats:

  • Params (M): 28.4
  • Input image size: 384 x 384

• Dataset: eu-common (707 classes)

  • Intermediate training involved ~4000 species from asia, europe and eastern africa

• Papers:

  • ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808

Model Usage

Image Classification

import birder
from birder.inference.classification import infer_image

(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)

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

# Create an inference transform
transform = birder.classification_transform(size, 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, num_classes), representing class probabilities.

Image Embeddings

import birder
from birder.inference.classification import infer_image

(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)

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

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

Detection Feature Map

from PIL import Image
import birder

(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)

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

# Create an inference transform
transform = birder.classification_transform(size, 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:
# [('stage1', torch.Size([1, 96, 96, 96])),
#  ('stage2', torch.Size([1, 192, 48, 48])),
#  ('stage3', torch.Size([1, 384, 24, 24])),
#  ('stage4', torch.Size([1, 768, 12, 12]))]

Citation

@misc{woo2023convnextv2codesigningscaling,
      title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, 
      author={Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie},
      year={2023},
      eprint={2301.00808},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2301.00808}, 
}