diff --git a/implementation.typ b/implementation.typ
index 1c60aac..24b046a 100644
--- a/implementation.typ
+++ b/implementation.typ
@@ -151,7 +151,7 @@ In a real world scenario this should not be the case because the support set is
 === Results
 The results of P>M>F look very promising and improve by a large margin over the ResNet50 method.
 In @pmfbottleperfa the model reached an accuracy of 79% with 5 shots / 4 way classification.
-The 2 way classification (faulty or not) performed even better and peaked at 94% accuracy with 5 shots.#todo[Add somehow that all classes are stacked]
+The 2 way classification (faulty or not) performed even better and peaked at 94% accuracy with 5 shots.
 
 Similar to the ResNet50 method in @resnet50perf the tests with an inbalanced class distribution performed worse than with balanced classes.
 So it is clearly a bad idea to add more good shots to the support set.
@@ -183,6 +183,9 @@ So it is clearly a bad idea to add more good shots to the support set.
 == CAML
 === Approach
 For the CAML implementation the pretrained model weights from the original paper were used.
+This brings the limitation of a maximum squence length to the non-causal sequence model.
+This is the reason why for this method the two imbalanced test cases couldn't be conducted.
+
 As a feture extractor a ViT-B/16 model was used, which is a Vision Transformer with a patch size of 16.
 This feature extractor was already pretrained when used by the authors of the original paper.
 For the non-causal sequence model a transformer model was used