From e872a70c84c5aa8396c49fa3460589ed830a22a8 Mon Sep 17 00:00:00 2001 From: statist32 Date: Wed, 3 Apr 2024 19:21:23 +0200 Subject: [PATCH] Fix broken link to supervised example --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9f34de69..48c3647d 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ With Tensorflow Similarity you can train two main types of models: 1. **Self-supervised models**: Used to learn general data representations on unlabeled data to boost the accuracy of downstream tasks where you have few labels. For example, you can pre-train a model on a large number of unlabled images using one of the supported contrastive methods supported by TensorFlow Similarity, and then fine-tune it on a small labeled dataset to achieve higher accuracy. To get started training your own self-supervised model see this [notebook](examples/unsupervised_hello_world.ipynb). -2. **Similarity models**: Output embeddings that allow you to find and cluster similar examples such as images representing the same object within a large corpus of examples. For instance, as visible above, you can train a similarity model to find and cluster similar looking, unseen cat and dog images from the [Oxford IIIT Pet Dataset](https://www.tensorflow.org/datasets/catalog/oxford_iiit_pet) while only training on a few of the dataset classes. To get started training your own similarity model see this [notebook](examples/supervised/visualization.ipynb). +2. **Similarity models**: Output embeddings that allow you to find and cluster similar examples such as images representing the same object within a large corpus of examples. For instance, as visible above, you can train a similarity model to find and cluster similar looking, unseen cat and dog images from the [Oxford IIIT Pet Dataset](https://www.tensorflow.org/datasets/catalog/oxford_iiit_pet) while only training on a few of the dataset classes. To get started training your own similarity model see this [notebook](examples/visualization.ipynb). ## What's new