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/resnet50.in1k_ft_df20_299", pretrained=True)
model = model.eval()
train_transforms = T.Compose([T.Resize((299, 299)),
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 (batch_size, num_features) shaped tensor# output is a (1, num_features) shaped tensor
Citation
@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}
}
Runs of BVRA resnet50.in1k_ft_df20_299 on huggingface.co
29
Total runs
6
24-hour runs
10
3-day runs
14
7-day runs
10
30-day runs
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