Revert ScaleDetection and LabelDetection to v1 models.

The v2 models were not performing well and should not be shipped. They need to be retrained but it is not required to do so alongside the new architecture.
vanvalenlab · Mar 1, 2021 · 030ba48 · 030ba48
1 parent 82302a7
commit 030ba48
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Showing 2 changed files with 12 additions and 14 deletions.
diff --git a/deepcell/applications/label_detection.py b/deepcell/applications/label_detection.py
@@ -41,7 +41,7 @@
 
 
 MODEL_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
-              'saved-models/LabelDetection-2.tar.gz')
+              'saved-models/LabelDetection-1.tar.gz')
 
 
 def LabelDetectionModel(input_shape=(None, None, 1),
@@ -119,22 +119,21 @@ class LabelDetection(Application):
 
     #: Metadata for the model and training process
     model_metadata = {
-        'batch_size': 32,
+        'batch_size': 64,
         'lr': 1e-3,
         'lr_decay': 0.9,
         'training_seed': 0,
-        'n_epochs': 20,
-        'precision_policy': 'mixed_float16',
-        'training_steps_per_epoch': 2653,
-        'validation_steps_per_epoch': 663
+        'n_epochs': 25,
+        'training_steps_per_epoch': 400,
+        'validation_steps_per_epoch': 100
     }
 
     def __init__(self, model=None):
 
         if model is None:
             archive_path = tf.keras.utils.get_file(
                 'LabelDetection.tgz', MODEL_PATH,
-                file_hash='3eb5900e6825abc6d2fa27d5b0a623c9',
+                file_hash='eadd047d599e58de91ff4ab1d735f4f0',
                 extract=True, cache_subdir='models'
             )
             model_path = os.path.splitext(archive_path)[0]

diff --git a/deepcell/applications/scale_detection.py b/deepcell/applications/scale_detection.py
@@ -41,7 +41,7 @@
 
 
 MODEL_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
-              'saved-models/ScaleDetection-2.tar.gz')
+              'saved-models/ScaleDetection-1.tar.gz')
 
 
 def ScaleDetectionModel(input_shape=(None, None, 1),
@@ -116,14 +116,13 @@ class ScaleDetection(Application):
 
     #: Metadata for the model and training process
     model_metadata = {
-        'batch_size': 128,
+        'batch_size': 64,
         'lr': 1e-3,
         'lr_decay': 0.9,
         'training_seed': 0,
-        'n_epochs': 20,
-        'precision_policy': 'mixed_float16',
-        'training_steps_per_epoch': 1199,
-        'validation_steps_per_epoch': 299,
+        'n_epochs': 200,
+        'training_steps_per_epoch': 8400,
+        'validation_steps_per_epoch': 2102,
         'error_rate': .01
     }
 
@@ -132,7 +131,7 @@ def __init__(self, model=None):
         if model is None:
             archive_path = tf.keras.utils.get_file(
                 'ScaleDetection.tgz', MODEL_PATH,
-                file_hash='516bac6d3f98b4fb345c035e88a6d0a4',
+                file_hash='1ca4ee3a90fd4445bb5484e10ac0081c',
                 extract=True, cache_subdir='models'
             )
             model_path = os.path.splitext(archive_path)[0]