---
library_name: transformers
tags:
- biology
- biodiversity
co2_eq_emissions:
  emissions: 240
  source: https://calculator.green-algorithms.org/
  training_type: pre-training
  geographical_location: Switzerland
  hardware_used: 1 v100 GPU
license: apache-2.0
datasets:
- Saving-Willy/Happywhale-kaggle
- Saving-Willy/test-sync
metrics:
- accuracy
pipeline_tag: image-classification
---

# Model Card for CetaceaNet

We provide a model for classifying whale species from images of their tails and fins.

## Model Details

The model takes as input a natural image of a cetacean and returns the three most probable cetacean species identified in this image.

### Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** HappyWhale
- **Shared by [optional]:** The Saving-Willy organization
- **Model type:** EfficientNet

### Model Sources

- **Repository:** https://github.com/knshnb/kaggle-happywhale-1st-place
- **Paper:** https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14167

## Uses

This model is intended for research use cases. It is intended to be fine-tuned on new data gathered by research institutions around the World.

### Downstream Use

We think that an interesting downstream use case would be identifying whale IDs based on our model (and future extensions of it).

### Out-of-Scope Use

This model is not intended to facilitate marine tourism or the exploitation of cetaceans in the wild and marine wildlife.

## How to Get Started with the Model

Install the necessary libraries to run our model (`transformers` and the extra requirements.txt):

```
pip install requirements.txt
```

Use the code below to get started with the model.

```
import cv2
from transformers import AutoModelForImageClassification

cetacean_classifier = AutoModelForImageClassification.from_pretrained("Saving-Willy/cetacean-classifier", trust_remote_code=True)
img = cv2.imread("tail.jpg")
predictions = cetacean_classifier(img)
```

## Training and Evaluation Details

To learn more about how the model was trained and evaluated, see [1st Place Solution of Kaggle Happywhale Competition](https://github.com/knshnb/kaggle-happywhale-1st-place).

## Citation

If you use this model in your research, please cite:

the original model authors:

```
@article{patton2023deep,
  title={A deep learning approach to photo--identification demonstrates high performance on two dozen cetacean species},
  author={Patton, Philip T and Cheeseman, Ted and Abe, Kenshin and Yamaguchi, Taiki and Reade, Walter and Southerland, Ken and Howard, Addison and Oleson, Erin M and Allen, Jason B and Ashe, Erin and others},
  journal={Methods in ecology and evolution},
  volume={14},
  number={10},
  pages={2611--2625},
  year={2023},
  publisher={Wiley Online Library}
}
```

the HappyWhale project:

```
@misc{happy-whale-and-dolphin,
    author = {Ted Cheeseman and Ken Southerland and Walter Reade and Addison Howard},
    title = {Happywhale - Whale and Dolphin Identification},
    year = {2022},
    howpublished = {\url{https://kaggle.com/competitions/happy-whale-and-dolphin}},
    note = {Kaggle}
}
```