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cetacean-classifier

Image Classification
Transformers
Safetensors
cetaceanet
biology
biodiversity
custom_code
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cetacean-classifier / README.md
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metadata
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.

  • Developed by: [More Information Needed]
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: EfficientNet
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

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.

[More Information Needed]

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).

[More Information Needed]

Out-of-Scope Use

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

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
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  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Model Card Contact

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