Beit Base Patch16 384.in1k Ft Fungitastic 384

🚀 BVRA/beit_base_patch16_384.in1k_ft_df24_384模型卡片

本模型主要用於丹麥真菌分類，能夠有效處理圖像分類相關任務，在生態、真菌研究等領域具有重要價值。

🚀 快速開始

圖像嵌入

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/beit_base_patch16_384.in1k_ft_df24_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 a (1, num_features) shaped tensor

✨ 主要特性

• 模型類型：丹麥真菌分類
• 模型統計信息：
  • 參數（百萬）：88.2
  • 圖像尺寸：384 x 384
• 訓練數據集：DF24 --> 點擊查看

📚 詳細文檔

引用信息

@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}
}

📄 許可證

本模型使用的許可證為CC BY-NC 4.0。