download=True

zoobot/pytorch/examples/finetuning/finetune_multiclass_classification.py

@@ -18,13 +18,14 @@
     num_workers= 8
     n_blocks = 1  # EffnetB0 is divided into 7 blocks. set 0 to only fit the head weights. Set 1, 2, etc to finetune deeper. 
     max_epochs = 6  #  6 epochs should get you ~93% accuracy. Set much higher (e.g. 1000) for harder problems, to use Zoobot's default early stopping. 
+    # the remaining key parameters for high accuracy are weight_decay, learning_rate, and lr_decay. You might like to tinker with these.
 
     # load in catalogs of images and labels to finetune on
     # each catalog should be a dataframe with columns of "id_str", "file_loc", and any labels
     # here I'm using galaxy-datasets to download some premade data - check it out for examples
 
-    train_catalog, _ = galaxy_mnist(root=data_dir, download=False, train=True)
-    test_catalog, _ = galaxy_mnist(root=data_dir, download=False, train=False)
+    train_catalog, _ = galaxy_mnist(root=data_dir, download=True, train=True)
+    test_catalog, _ = galaxy_mnist(root=data_dir, download=True, train=False)
 
     # wondering about "label_cols"? 
     # This is a list of catalog columns which should be used as labels