augmentation learning

test_transform_exploration.py

@@ -59,13 +59,14 @@ def get_transform(use_hflip, use_vflip, rotation, contrast, resample):
     if use_vflip:
         transform_list.append(transforms.RandomVerticalFlip())
     # rotation
-    transform_list.append(transforms.RandomRotation(degrees=rotation))
+    transform_list.append(transforms.RandomRotation(degrees=rotation, expand=True)) # expand=True: esnure that the whole image is represented on the rotated image
     # contrast
     if contrast > 0:
         transform_list.append(v2.ColorJitter(contrast=contrast))
     # resample
     if resample != 1:
         transform_list.append(RandomResample(scale_factor=resample))
+    transform_list.append(transforms.Resize(224))
     transform_list.append(transforms.ToTensor())
     return transforms.Compose(transform_list)
 

train_results.txt

@@ -182,4 +182,63 @@ transforms.ToTensor()
         balanced accuracy: 98.77%
         size: 43MB
     checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_augmentation4
-    note: as expected: much better results, but the learning curves still show overfitting
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+    note: as expected: much better results, but the learning curves still show overfitting
+
+augmentation using horizontal and vertical flips: 
+MODEL_NAME = 'resnet18_flips'
+transforms.v2.RandomHorizontalFlip(p=0.5),
+transforms.v2.RandomVerticalFlip(p=0.5),
+transforms.ToTensor()
+    time: 20 min 16 sec
+    Best epoch: 22
+        validation accuracy: 0.9294
+        validation loss: 1.9463
+        test accuracy: 98.71%
+        balanced accuracy: 98.73%
+        size: 43MB
+    checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips
+
+augmentation using horizontal, vertical flips and 90 degrees random rotation: 
+MODEL_NAME = 'resnet18_flips_90'
+transforms.v2.RandomHorizontalFlip(p=0.5),
+transforms.v2.RandomVerticalFlip(p=0.5),
+transforms.v2.RandomRotation(degrees=90, expand=True),
+transforms.ToTensor()
+    time: 20 min 19 sec
+    Best epoch: 13
+        validation accuracy: 89.24%
+        validation loss: 1.8287
+        test accuracy: 93.27%
+        balanced accuracy: 95.00%
+        size: 43
+    checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_90
+
+augmentation using horizontal, vertical flips and 180 degrees random rotation: 
+MODEL_NAME = 'resnet18_flips_180'
+transforms.v2.RandomHorizontalFlip(p=0.5),
+transforms.v2.RandomVerticalFlip(p=0.5),
+transforms.v2.RandomRotation(degrees=180, expand=True),
+transforms.ToTensor()
+    time: 20 min 12 sec
+    Best epoch: 22
+        validation accuracy: 1.7595
+        validation loss: 90.17%
+        test accuracy: 95.95%
+        balanced accuracy: 95.60%
+        size: 43
+    checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_180
+
+augmentation using horizontal, vertical flips and 360 degrees random rotation: 
+MODEL_NAME = 'resnet18_flips_360'
+transforms.v2.RandomHorizontalFlip(p=0.5),
+transforms.v2.RandomVerticalFlip(p=0.5),
+transforms.v2.RandomRotation(degrees=360, expand=True),
+transforms.ToTensor()
+    time: 20 min 12 sec
+    Best epoch: 22
+        validation accuracy: 1.8767
+        validation loss: 90.58%
+        test accuracy: 95.13%
+        balanced accuracy: 95.60%
+        size: 43
+    checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_360
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