MegaDescriptor
Collection
A set of models for re-identification of a broad range of animal species.
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5 items
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Updated
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4
A Swin-L image feature model. Superwisely pre-trained on animal re-identification datasets.
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-L-384", pretrained=True)
model = model.eval()
train_transforms = T.Compose([T.Resize(size=(384, 384)),
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
@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}
}