---
tags:
- image-classification
library_name: wildlife-datasets
license: cc-by-nc-4.0
---

# Model card for MegaDescriptor-EfficientNetB3
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}
}
```