diff --git a/docs/python-tutorial.md b/docs/python-tutorial.md index 253166b..94a3c85 100644 --- a/docs/python-tutorial.md +++ b/docs/python-tutorial.md @@ -146,14 +146,14 @@ The following notebooks show methods to fine-tune BioCLIP for classification. fine-tunes BioCLIP by combining a [RidgeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html) with BioCLIP image embeddings. Open In Colab -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). - - [FineTuneSimpleShot.ipynb](https://github.com/Imageomics/pybioclip/blob/main/examples/FineTuneSimpleShot.ipynb) fine-tunes BioCLIP by combining a [SimpleShot](https://arxiv.org/abs/1911.04623) classifier with BioCLIP image embeddings. Open In Colab +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). When a list of PIL images is passed the index of the image will be filled in for `file_name`. This is because PIL images may not have an associated file name.