First model version

README.md

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+---
+tags:
+- image-classification
+library_name: wildlife-datasets
+license: cc-by-nc-4.0
+---
+# Model card for vit_small_patch14_dinov2.lvd142m
+
+A EfficientNetB3 based image feature model. Supervisely pre-trained on animal re-identification datasets.
+
+
+## Model Details
+- **Model Type:** Animal re-identification / feature backbone
+- **Model Stats:**
+  - Params (M): 12.2
+  - GMACs: 1.6
+  - Activations (M): 21.5
+  - Image size: 288 x 288
+- **Papers:**
+  - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946
+
+## 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-EfficientNetB3", pretrained=True)
+model = model.eval()
+
+transforms = T.Compose([T.Resize(288), 
+                              T.ToTensor(), 
+                              T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) 
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+output = model(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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+{
+  "url": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth",
+  "hf_hub_id": "timm/efficientnet_b3.ra2_in1k",
+  "architecture": "efficientnet_b3",
+  "tag": "ra2_in1k",
+  "custom_load": false,
+  "input_size": [
+      3,
+      288,
+      288
+  ],
+  "test_input_size": [
+      3,
+      320,
+      320
+  ],
+  "fixed_input_size": false,
+  "interpolation": "bicubic",
+  "crop_pct": 0.875,
+  "test_crop_pct": 1.0,
+  "crop_mode": "center",
+  "mean": [
+      0.485,
+      0.456,
+      0.406
+  ],
+  "std": [
+      0.229,
+      0.224,
+      0.225
+  ],
+  "num_classes": 1000,
+  "pool_size": [
+      9,
+      9
+  ],
+  "first_conv": "conv_stem",
+  "classifier": "classifier"
+}

pytorch_model.bin

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