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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 BVRA/DF23M-tf_efficientnet_b0.ap_in1k-384
+
+## Model Details
+- **Model Type:** tf_efficientnet_b0.ap_in1k
+- **Model Stats:**
+  - Params (M): ??
+  - Image size: 384 x 384
+- **Papers:**
+- **Original:** ??
+- **Train Dataset:** DF23M --> 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:BVRA/DF23M-tf_efficientnet_b0.ap_in1k-384", pretrained=True)
+model = model.eval()
+train_transforms = T.Compose([T.Resize(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
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+https://openaccess.thecvf.com/content/WACV2022/papers/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.pdf