Augmentation effect

Baseline Model

With Histogram Equalization

• General parameter:

Parameter	Metrics
batch_size: 32	f1-score: -
hist_equalization: True	mse: -
image_resolution:(512, 512)	optimal mse threshold: -
num_epochs: 1000	roc-auc: 0.4541

Without Histogram Equalization

• General parameter:

Parameter	Metrics
batch_size: 32	f1-score: -
hist_equalization: True	mse: -
image_resolution:(512, 512)	optimal mse threshold: -
num_epochs: 1000	roc-auc: 0.4541

Augmentation Effects

Augmentation 1: 5 different methods

• General parameter:

Parameter	Metrics
batch_size: 32	f1-score: 0.737247353224254
hist_equalization: True	mse: 0.0024145432
image_resolution:(512, 512)	optimal mse threshold: 0.00030937084
num_epochs: 1000	roc-auc: 0.4567827733624078

• Augmentation parameter:

Chance	Parameter	Augmentation
1	iaa.Fliplr(0.5)	Flip horizontally with 50%
1	iaa.Flipud(0.1)	Flip vertically with 10%
0.5	iaa.Affine(rotate=(-15, 15))	Rotate from -15° to 15°
0.5	iaa.Multiply((0.8, 1.2))	Multiply Values from 0.8 to 1.2 → augment brighness
0.5	iaa.Affine(scale=(0.8, 1.2)))	Zoom in and out from 0.8 to 1.2

• Last image:

• Loss

  • Does not overfit.
  • Some peaks in the training loss
  • Validation loss is twice as high as training loss → train longer

• False positive: highest MSE
  • Have a lot of noisy space around the hand
  • Not common angles
  • Black image

• False negative: lowest MSE
  • Common angles
  • Space around the hand is not to noisy

• True negative: lowest MSE

• True positive: highest MSE
  • Images with a lot of noise
  • Uncommon angles

Augmentation 2: Flip Hand

• General parameter:

Parameter	Metrics
batch_size: 32	f1-score: 0.7373493975903614
hist_equalization: True	mse: 0.0021393178
image_resolution:(256, 256)	optimal mse threshold: 0.0005970913
num_epochs: 500	roc-auc: 0.45168714041362146

• Augmentation parameter:

Parameter	Augmentation
iaa.Fliplr(0.5)	Flip horizontally with 50%

• Last image

• Loss
  • Does not overfit.

• False positive: highest MSE
  • Have a lot of noisy space around the hand
  • Not common angles
  • Black image

• False negative: lowest MSE
  • Common angles
  • Space around the hand is not to noisy

• True negative: lowest MSE

• True positive: highest MSE
  • Images with a lot of noise
  • Uncommon angles