diff --git a/tests/model_tests/chestnut_dec_may/train.py b/tests/model_tests/chestnut_dec_may/train.py
index 847ef65..a23cbcf 100644
--- a/tests/model_tests/chestnut_dec_may/train.py
+++ b/tests/model_tests/chestnut_dec_may/train.py
@@ -18,7 +18,7 @@
 from sklearn.preprocessing import StandardScaler, OrdinalEncoder
 
 from frdc.load.preset import FRDCDatasetPreset as ds
-from frdc.models.inceptionv3 import InceptionV3MixMatchModule
+from frdc.models.efficientnetb1 import EfficientNetB1MixMatchModule
 from frdc.train.frdc_datamodule import FRDCDataModule
 from frdc.utils.training import predict, plot_confusion_matrix
 from model_tests.utils import (
@@ -61,11 +61,14 @@ def main(
     wandb_project="frdc",
 ):
     # Prepare the dataset
-    train_lab_ds = ds.chestnut_20201218(transform=train_preprocess_augment)
+    im_size = 299
+    train_lab_ds = ds.chestnut_20201218(
+        transform=train_preprocess_augment(im_size)
+    )
     train_unl_ds = ds.chestnut_20201218.unlabelled(
-        transform=train_unl_preprocess(2)
+        transform=train_unl_preprocess(im_size, 2)
     )
-    val_ds = ds.chestnut_20210510_43m(transform=val_preprocess)
+    val_ds = ds.chestnut_20210510_43m(transform=val_preprocess(im_size))
 
     # Prepare the datamodule and trainer
     dm = FRDCDataModule(
@@ -103,13 +106,13 @@ def main(
     oe = get_y_encoder(train_lab_ds.targets)
     ss = get_x_scaler(train_lab_ds.ar_segments)
 
-    m = InceptionV3MixMatchModule(
+    m = EfficientNetB1MixMatchModule(
         in_channels=train_lab_ds.ar.shape[-1],
         n_classes=len(oe.categories_[0]),
         lr=lr,
         x_scaler=ss,
         y_encoder=oe,
-        imagenet_scaling=True,
+        frozen=True,
     )
 
     trainer.fit(m, datamodule=dm)
@@ -125,10 +128,9 @@ def main(
             "chestnut_nature_park",
             "20210510",
             "90deg43m85pct255deg",
-            transform=val_preprocess,
+            transform=val_preprocess(im_size),
         ),
-        model_cls=InceptionV3MixMatchModule,
-        ckpt_pth=Path(ckpt.best_model_path),
+        model=m,
     )
     fig, ax = plot_confusion_matrix(y_true, y_pred, oe.categories_[0])
     acc = np.sum(y_true == y_pred) / len(y_true)
@@ -152,6 +154,6 @@ def main(
         epochs=EPOCHS,
         train_iters=TRAIN_ITERS,
         lr=LR,
-        wandb_name="Try with Inception Unfrozen & Random Erasing",
+        wandb_name="EfficientNet 299x299",
         wandb_project="frdc-dev",
     )