Create README.md

README.md

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+---
+tags:
+- image-classification
+- ecology
+- animals
+- re-identification
+library_name: wildlife-datasets
+license: cc-by-nc-4.0
+---
+# Model card for MegaDescriptor-B-224
+
+A Swin-L image feature model. Superwisely pre-trained on animal re-identification datasets.
+
+
+## Model Details
+- **Model Type:** Animal re-identification / feature backbone
+- **Model Stats:**
+  - Params (M): ??
+  - Image size: 384 x 384
+- **Papers:**
+  - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows --> https://arxiv.org/abs/2103.14030
+- **Original:** ??
+- **Pretrain Dataset:** All available re-identification datasets --> TBD
+
+## 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/wildlife-mega", pretrained=True)
+model = model.eval()
+
+train_transforms = T.Compose([T.Resize(224), 
+                              T.ToTensor(), 
+                              T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) 
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+output = model(train_transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+
+```bibtex
+TBD
+```