---
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-B image feature model. Supervisely pre-trained on animal re-identification datasets.


## Model Details
- **Model Type:** Animal re-identification / feature backbone
- **Model Stats:**
  - Params (M): 109.1
  - Image size: 224 x 224
  - Architecture: swin_base_patch4_window7_224
- **Papers:**
  - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows --> https://arxiv.org/abs/2103.14030
- **Original:** ??
- **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-B-224", 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
```