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---
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}
}
```
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