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Add Fine-tune RidgeClassifier example notebook #79

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Add Fine-tune RidgeClassifier example notebook #79

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15 changes: 11 additions & 4 deletions docs/python-tutorial.md
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Expand Up @@ -136,12 +136,19 @@ small 7.165559509303421e-05
[GradCamExperiment.ipynb](https://github.com/Imageomics/pybioclip/blob/main/examples/GradCamExperiment.ipynb) applies GradCAM AI explainability to BioCLIP. <a target="_blank" href="https://colab.research.google.com/github/Imageomics/pybioclip/blob/main/examples/GradCamExperiment.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

### Fine-tune with SVM
[FineTuneSVM.ipynb](https://github.com/Imageomics/pybioclip/blob/main/examples/FineTuneSVM.ipynb) Fine-tunes BioCLIP by combining an [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) with BioCLIP image embeddings. <a target="_blank" href="https://colab.research.google.com/github/Imageomics/pybioclip/blob/main/examples/FineTuneSVM.ipynb">
### Fine-tune
The following notebooks show methods to fine-tune BioCLIP for classification.

- [FineTuneSVM.ipynb](https://github.com/Imageomics/pybioclip/blob/main/examples/FineTuneSVM.ipynb) fine-tunes BioCLIP by combining an [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) with BioCLIP image embeddings. <a target="_blank" href="https://colab.research.google.com/github/Imageomics/pybioclip/blob/main/examples/FineTuneSVM.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

- [FineTuneRidgeClassifier.ipynb](https://github.com/Imageomics/pybioclip/blob/main/examples/FineTuneRidgeClassifier.ipynb)
fine-tunes BioCLIP by combining a [RidgeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html) with BioCLIP image embeddings. <a target="_blank" href="https://colab.research.google.com/github/Imageomics/pybioclip/blob/main/examples/FineTuneRidgeClassifier.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
As can be seen from comparing the confusion matrices, fine-tuning an SVM classifier on BioCLIP image embeddings may yield better results than using BioCLIP in "zero-shot mode", i.e., predicting on a list of custom labels.

This work is based on code from [biobench](https://github.com/samuelstevens/biobench).
As can be seen from comparing the confusion matrices in the notebooks, fine-tuning may yield better results than using BioCLIP in "zero-shot mode", i.e., predicting on a list of custom labels.

This work is based on code from [biobench](https://github.com/samuelstevens/biobench).

## PIL Images
The predict() functions used in all the examples above allow passing a list of paths or a list of [PIL Images](https://pillow.readthedocs.io/en/stable/reference/Image.html).
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