FungiTastic
Collection
A collection of models pre-trained on the FungiTastic -- ClosedSet dataset.
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13 items
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Updated
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/convnext_base.in1k_ft_df24_224", pretrained=True)
model = model.eval()
train_transforms = T.Compose([T.Resize((224, 224)),
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
@InProceedings{Picek_2022_WACV,
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},
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, Luk{'a}{{s}} and {{S}}ulc, Milan and Matas, Ji{{r}}{'\i} 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}
}