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1import timm
2import torch
3import torchvision.transforms as T
4from PIL import Image
5from urllib.request import urlopen
6model = timm.create_model("hf-hub:BVRA/convnext_base.in1k_ft_fungitastic-mini_224", pretrained=True)
7model = model.eval()
8train_transforms = T.Compose([T.Resize((224, 224)),
9 T.ToTensor(),
10 T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
11img = Image.open(PATH_TO_YOUR_IMAGE)
12output = model(train_transforms(img).unsqueeze(0))
13# output is a (1, num_features) shaped tensor1@article{picek2024fungitastic,
2 title={FungiTastic: A multi-modal dataset and benchmark for image categorization},
3 author={Picek, Lukas and Janouskova, Klara and Sulc, Milan and Matas, Jiri},
4 journal={arXiv preprint arXiv:2408.13632},
5 year={2024}
6}1@InProceedings{Picek_2022_WACV,
2 author = {Picek, Luk'a {s} and {S}ulc, Milan and Matas, Ji {r}{'\i} and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias},
3 title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset},
4 booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
5 month = {January},
6 year = {2022},
7 pages = {1525-1535}
8}1@article{picek2022automatic,
2 title={Automatic Fungi Recognition: Deep Learning Meets Mycology},
3 author={Picek, Luk{'a}{ {s}} and { {S}}ulc, Milan and Matas, Ji{ {r}}{'\i} and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil},
4 journal={Sensors},
5 volume={22},
6 number={2},
7 pages={633},
8 year={2022},
9 publisher={Multidisciplinary Digital Publishing Institute}
10}