---
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
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 returns the three most probable cetacean species identified in the input 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.

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- **Model type:** EfficientNet
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### Model Sources [optional]

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## 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.

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### Downstream Use [optional]

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

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### Out-of-Scope Use

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

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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## Glossary [optional]

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## Model Card Authors [optional]

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