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README.md

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+
+---
+tags:
+- image-classification
+- ecology
+- fungi
+- FGVC
+library_name: DanishFungi
+license: cc-by-nc-4.0
+---
+# Model card for MHanzl/tf_efficientnet_b0.in1k_ft_df24m_224
+
+## Model Details
+- **Model Type:** Danish Fungi Classification 
+- **Model Stats:**
+  - Params (M): 4.2
+  - Image size: 224 x 224
+- **Papers:**
+- **Original:** ??
+- **Train Dataset:** DF24M --> https://sites.google.com/view/danish-fungi-dataset
+
+## Model Usage
+### Image Embeddings
+```python
+import timm
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from urllib.request import urlopen
+model = timm.create_model("hf-hub:MHanzl/tf_efficientnet_b0.in1k_ft_df24m_224", pretrained=True)
+model = model.eval()
+train_transforms = T.Compose([T.Resize((224, 224)), 
+                              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
+```
+
+## Citation 
+```bibtex
+@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}
+}
+```
+```bibtex
+@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}
+}
+```