Duplicate from BVRA/MegaDescriptor-L-384

Co-authored-by: V Cermak <cermakvo@users.noreply.huggingface.co>

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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-L-384
+
+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): 228.8
+  - Image size: 384 x 384
+  - Architecture: swin_large_patch4_window12_384
+- **Paper:** [WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification](https://openaccess.thecvf.com/content/WACV2024/html/Cermak_WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification_WACV_2024_paper.html)
+- **Related Papers:**
+  - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+  - [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/pdf/2304.07193.pdf)
+- **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
+
+## 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/MegaDescriptor-L-384", pretrained=True)
+model = model.eval()
+
+train_transforms = T.Compose([T.Resize(size=(384, 384)),
+                              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
+@inproceedings{vcermak2024wildlifedatasets,
+  title={WildlifeDatasets: An open-source toolkit for animal re-identification},
+  author={{\v{C}}erm{\'a}k, Vojt{\v{e}}ch and Picek, Lukas and Adam, Luk{\'a}{\v{s}} and Papafitsoros, Kostas},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
+  pages={5953--5963},
+  year={2024}
+}
+```

config.json

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+{
+  "architecture": "swin_large_patch4_window12_384",
+  "num_classes": 0,
+  "num_features": 1536,
+  "global_pool": "avg",
+  "pretrained_cfg": {
+    "custom_load": false,
+    "input_size": [
+      3,
+      384,
+      384
+    ],
+    "fixed_input_size": true,
+    "interpolation": "bicubic",
+    "crop_pct": 0.9,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 0,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "patch_embed.proj",
+    "license": "mit",
+    "classifier": "head"
+  }
+}

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