Update cellfinder batch sizes to match new defaults (#196)

brainglobe · Jun 4, 2024 · 2269d99 · 2269d99
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diff --git a/docs/source/documentation/brainglobe-workflows/brainmapper/classification.md b/docs/source/documentation/brainglobe-workflows/brainmapper/classification.md
@@ -6,7 +6,7 @@
 * `--model-weights` To use pretrained model weights. Ensure that this model matches the `--network-depth` parameter.
 * `--network-depth`. Resnet depth \(based on [He et al. \(2015\)](https://arxiv.org/abs/1512.03385)\) **Default: 50**
 * `--batch-size` Batch size for classification. Can be adjusted depending on GPU memory. This can often be increased 
-on high-memory modern GPUS \(e.g. 128 works well on a Titan RTX\). **Default: 32**
+on high-memory modern GPUS \(e.g. 128 works well on a Titan RTX\). **Default: 64**
 
 _**You shouldn't need to change these:**_
 

diff --git a/docs/source/documentation/cellfinder/user-guide/cellfinder-core.md b/docs/source/documentation/cellfinder/user-guide/cellfinder-core.md
@@ -88,7 +88,7 @@ end_plane = -1
 trained_model = None
 model_weights = None
 model = "resnet50_tv"
-batch_size = 32
+batch_size = 64
 n_free_cpus = 2
 network_voxel_sizes = [5, 1, 1]
 soma_diameter = 16
@@ -169,7 +169,7 @@ run_training(
     learning_rate=0.0001,
     continue_training=True, # by default use supplied model
     test_fraction=0.1,
-    batch_size=32,
+    batch_size=16,
     save_progress=True,
     epochs=10,
 )