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--- |
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library_name: transformers |
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tags: |
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- biology |
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- biodiversity |
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co2_eq_emissions: |
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emissions: 240 |
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source: https://calculator.green-algorithms.org/ |
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training_type: pre-training |
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geographical_location: Switzerland |
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hardware_used: 1 v100 GPU |
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license: apache-2.0 |
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datasets: |
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- Saving-Willy/Happywhale-kaggle |
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- Saving-Willy/test-sync |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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--- |
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# Model Card for CetaceaNet |
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We provide a model for classifying whale species from images of their tails and fins. |
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## Model Details |
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The model takes as input a natural image of a cetacean and returns the three most probable cetacean species identified in this image. |
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### Model Description |
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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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- **Developed by:** HappyWhale |
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- **Shared by [optional]:** The Saving-Willy organization |
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- **Model type:** EfficientNet |
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### Model Sources |
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- **Repository:** https://github.com/knshnb/kaggle-happywhale-1st-place |
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- **Paper:** https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14167 |
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## Uses |
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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 |
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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 |
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This model is not intended to facilitate marine tourism or the exploitation of cetaceans in the wild and marine wildlife. |
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## How to Get Started with the Model |
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Install the necessary libraries to run our model (`transformers` and the extra requirements.txt): |
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``` |
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pip install requirements.txt |
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``` |
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Use the code below to get started with the model. |
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``` |
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import cv2 |
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from transformers import AutoModelForImageClassification |
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cetacean_classifier = AutoModelForImageClassification.from_pretrained("Saving-Willy/cetacean-classifier", trust_remote_code=True) |
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img = cv2.imread("tail.jpg") |
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predictions = cetacean_classifier(img) |
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``` |
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## Training and Evaluation Details |
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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). |
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## Citation |
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If you use this model in your research, please cite: |
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the original model authors: |
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``` |
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@article{patton2023deep, |
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title={A deep learning approach to photo--identification demonstrates high performance on two dozen cetacean species}, |
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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}, |
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journal={Methods in ecology and evolution}, |
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volume={14}, |
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number={10}, |
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pages={2611--2625}, |
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year={2023}, |
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publisher={Wiley Online Library} |
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} |
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``` |
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the HappyWhale project: |
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``` |
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@misc{happy-whale-and-dolphin, |
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author = {Ted Cheeseman and Ken Southerland and Walter Reade and Addison Howard}, |
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title = {Happywhale - Whale and Dolphin Identification}, |
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year = {2022}, |
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howpublished = {\url{https://kaggle.com/competitions/happy-whale-and-dolphin}}, |
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note = {Kaggle} |
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} |
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``` |
