Model card for BVRA/tf_efficientnet_b3.in1k_ft_df20m_384

Model Details

• Model Type: Danish Fungi Classification
• Model Stats:
  • Params (M): 11M
  • Image size: 384 x 384
• Papers:
  • Original : Deep Residual Learning for Image Recognition --> https://arxiv.org/pdf/1512.03385
  • Train Dataset: DF20 --> https://github.com/BohemianVRA/DanishFungiDataset/

Model Usage

Image Embeddings

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/tf_efficientnet_b3.in1k_ft_df20m_384", pretrained=True)
model = model.eval()
train_transforms = T.Compose([T.Resize((384, 384)), 
                              T.ToTensor(), 
                              T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 
img = Image.open(PATH_TO_YOUR_IMAGE)
output = model(train_transforms(img).unsqueeze(0))  # output is (batch_size, 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}
}