--- tags: - image-classification - ecology - animals - re-identification library_name: wildlife-datasets license: cc-by-nc-4.0 --- # Model card for MegaDescriptor-S-224 A Swin-S image feature model. Supervisely pre-trained on animal re-identification datasets. ## Model Details - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 49.6 - Image size: 224 x 224 - Architecture: swin_small_patch4_window7_224 - **Paper:** [WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification](https://openaccess.thecvf.com/content/WACV2024/html/Cermak_WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification_WACV_2024_paper.html) - **Related Papers:** - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) - [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/pdf/2304.07193.pdf) - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets ## Model Usage ### Image Embeddings ```python 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-S-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 ```bibtex @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} } ```