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README.md

@@ -1,3 +1,4 @@
+
 ---
 tags:
 - image-classification
@@ -6,38 +7,34 @@ tags:
 library_name: DanishFungi
 license: cc-by-nc-4.0
 ---
-    # Model card for MHanzl/DF23M-mobilenetv2_100.ra_in1k_224
-
-    A Swin-S image feature model. Superwisely pre-trained on animal re-identification datasets.
-
+# Model card for MHanzl/DF23M-mobilenetv2_100.ra_in1k_224
 
-    ## Model Details
-    - **Model Type:** Danish Fungi Classification 
-    - **Model Stats:**
-      - Params (M): ??
-      - Image size: [224, 224] x [224, 224]
-    - **Papers:**
-    - **Original:** ??
-    - **Train Dataset:** DF23M --> https://sites.google.com/view/danish-fungi-dataset
+## Model Details
+- **Model Type:** Danish Fungi Classification 
+- **Model Stats:**
+  - Params (M): ??
+  - Image size: [224, 224] x [224, 224]
+- **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:MHanzl/DF23M-mobilenetv2_100.ra_in1k_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 (batch_size, num_features) shaped tensor
-    # output is a (1, num_features) shaped tensor
-    ```
+## 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/DF23M-mobilenetv2_100.ra_in1k_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 (batch_size, num_features) shaped tensor
+# output is a (1, num_features) shaped tensor
+```
 
-    ## Citation
+## Citation
 
-