metadata

tags:
  - image-classification
  - ecology
  - animals
  - re-identification
library_name: wildlife-datasets
license: cc-by-nc-4.0

Model card for MegaDescriptor-B-224

A Swin-B image feature model. Supervisely pre-trained on animal re-identification datasets.

Model Details

Model Type: Animal re-identification / feature backbone
Model Stats:
- Params (M): 109.1
- Image size: 224 x 224
- Architecture: swin_base_patch4_window7_224
Paper: WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification
Related Papers:
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
- DINOv2: Learning Robust Visual Features without Supervision
Pretrain Dataset: All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets

Model Usage

Image Embeddings


import timm
import torch
import torchvision.transforms as T

from PIL import Image
from urllib.request import urlopen

model = timm.create_model("hf-hub:BVRA/MegaDescriptor-B-224", pretrained=True)
model = model.eval()

train_transforms = T.Compose([T.Resize(224), 
                              T.ToTensor(), 
                              T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 

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

output = model(train_transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
# output is a (1, num_features) shaped tensor

Citation

@inproceedings{vcermak2024wildlifedatasets,
  title={WildlifeDatasets: An open-source toolkit for animal re-identification},
  author={{\v{C}}erm{\'a}k, Vojt{\v{e}}ch and Picek, Lukas and Adam, Luk{\'a}{\v{s}} and Papafitsoros, Kostas},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={5953--5963},
  year={2024}
}