--- tags: - image-classification - ecology - fungi - FGVC library_name: DanishFungi license: cc-by-nc-4.0 --- # Model card for BVRA/legacy_seresnext101_32x4d.in1k_ft_df20_384 ## Model Details - **Model Type:** Danish Fungi Classification - **Model Stats:** - Params (M): 50.2M - Image size: 384 x 384 - **Papers:** - **Original 1:** Aggregated Residual Transformations for Deep Neural Networks --> https://arxiv.org/pdf/1611.05431v2 - **Original 2:** Squeeze-and-Excitation Networks --> https://arxiv.org/pdf/1709.01507v4 - **Train Dataset:** DF20 --> https://github.com/BohemianVRA/DanishFungiDataset/ ## 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/legacy_seresnext101_32x4d.in1k_ft_df20_384", pretrained=True) model = model.eval() train_transforms = T.Compose([T.Resize((384, 384)), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) img = Image.open(PATH_TO_YOUR_IMAGE) output = model(train_transforms(img).unsqueeze(0)) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @InProceedings{Picek_2022_WACV, author = {Picek, Lukas and Sulc, Milan and Matas, Jiri and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {1525-1535} } @article{picek2022automatic, title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, author={Picek, Lukas and Sulc, Milan and Matas, Jiri and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, journal={Sensors}, volume={22}, number={2}, pages={633}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ```