From 94624a1c70980c7d1c15a7b7fdc743297bf01c9c Mon Sep 17 00:00:00 2001
From: Mike Walmsley <walmsleymk1@gmail.com>
Date: Thu, 4 Apr 2024 17:43:46 -0400
Subject: [PATCH] syntax hotfix, add mailing list

---
 README.md | 58 +++++++++++++++++++++++++++----------------------------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index fd9cb351..1c6220bc 100755
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ Zoobot is trained using millions of answers by Galaxy Zoo volunteers. This code
 - [Pretrained Weights](https://zoobot.readthedocs.io/en/latest/pretrained_models.html)
 - [Datasets](https://www.github.com/mwalmsley/galaxy-datasets)
 - [Documentation](https://zoobot.readthedocs.io/) (for understanding/reference)
+- [Mailing List](https://groups.google.com/g/zoobot) (for updates)
 
 ## Installation
 
@@ -59,44 +60,43 @@ The [Colab notebook](https://colab.research.google.com/drive/1A_-M3Sz5maQmyfW2A7
 
 Let's say you want to find ringed galaxies and you have a small labelled dataset of 500 ringed or not-ringed galaxies. You can retrain Zoobot to find rings like so:
 
-    ```python
+```python
+import pandas as pd
+from galaxy_datasets.pytorch.galaxy_datamodule import GalaxyDataModule
+from zoobot.pytorch.training import finetune
 
-    import pandas as pd
-    from galaxy_datasets.pytorch.galaxy_datamodule import GalaxyDataModule
-    from zoobot.pytorch.training import finetune
+# csv with 'ring' column (0 or 1) and 'file_loc' column (path to image)
+labelled_df = pd.read_csv('/your/path/some_labelled_galaxies.csv')
 
-    # csv with 'ring' column (0 or 1) and 'file_loc' column (path to image)
-    labelled_df = pd.read_csv('/your/path/some_labelled_galaxies.csv')
+datamodule = GalaxyDataModule(
+    label_cols=['ring'],
+    catalog=labelled_df,
+    batch_size=32
+)
 
-    datamodule = GalaxyDataModule(
-      label_cols=['ring'],
-      catalog=labelled_df,
-      batch_size=32
-    )
+# load trained Zoobot model
+model = finetune.FinetuneableZoobotClassifier(checkpoint_loc, num_classes=2)  
 
-    # load trained Zoobot model
-    model = finetune.FinetuneableZoobotClassifier(checkpoint_loc, num_classes=2)  
-    
-    # retrain to find rings
-    trainer = finetune.get_trainer(save_dir)
-    trainer.fit(model, datamodule)
-    ```
+# retrain to find rings
+trainer = finetune.get_trainer(save_dir)
+trainer.fit(model, datamodule)
+```
 
 Then you can make predict if new galaxies have rings:
 
-    ```python
-    from zoobot.pytorch.predictions import predict_on_catalog
+```python
+from zoobot.pytorch.predictions import predict_on_catalog
 
-    # csv with 'file_loc' column (path to image). Zoobot will predict the labels.
-    unlabelled_df = pd.read_csv('/your/path/some_unlabelled_galaxies.csv')
+# csv with 'file_loc' column (path to image). Zoobot will predict the labels.
+unlabelled_df = pd.read_csv('/your/path/some_unlabelled_galaxies.csv')
 
-    predict_on_catalog.predict(
-      unlabelled_df,
-      model,
-      label_cols=['ring'],  # only used for 
-      save_loc='/your/path/finetuned_predictions.csv'
-    )
-    ```
+predict_on_catalog.predict(
+    unlabelled_df,
+    model,
+    label_cols=['ring'],  # only used for 
+    save_loc='/your/path/finetuned_predictions.csv'
+)
+```
 
 Zoobot includes many guides and working examples - see the [Getting Started](#getting-started) section below.