add images for final results

experimentalresults.typ

@@ -1,15 +1,41 @@
+#import "utils.typ": todo
+#import "@preview/subpar:0.1.1"
+
 = Experimental Results
 
 == Is Few-Shot learning a suitable fit for anomaly detection?
+_Should Few-Shot learning be used for anomaly detection tasks?
+How does it compare to well established algorithms such as Patchcore or EfficientAD?_
 
-Should Few-Shot learning be used for anomaly detection tasks?
-How does it compare to well established algorithms such as Patchcore or EfficientAD?
+@comparison2waybottle shows the performance of the 2-way classification (anomaly or not) on the bottle class and @comparison2waycable the same on the cable class.
+The performance values are the same as in @experiments but just merged together into one graph.
+As a reference Patchcore reaches an AUROC score of 99.6% and EfficientAD reaches 99.8% averaged over all classes provided by the MVTec AD dataset.
+Both are trained with samples from the 'good' class only.
+So there is a clear performance gap between Few-Shot learning and the state of the art anomaly detection algorithms.
+
+That means if the goal is just to detect anomalies, Few-Shot learning is not the best choice and Patchcore or EfficientAD should be used.
+
+#subpar.grid(
+  figure(image("rsc/comparison-2way-bottle.png"), caption: [
+    Bottle class
+  ]), <comparison2waybottle>,
+  figure(image("rsc/comparison-2way-cable.png"), caption: [
+    Cable class
+  ]), <comparison2waycable>,
+  columns: (1fr, 1fr),
+  caption: [2-Way classification performance],
+  label: <comparison2way>,
+)
 
 == How does disbalancing the Shot number affect performance?
-Does giving the Few-Shot learner more good than bad samples improve the model performance?
+_Does giving the Few-Shot learner more good than bad samples improve the model performance?_
+
+As all three method results in @experiments show, the performance of the Few-Shot learner decreases with an increasing number of good samples.
+Which is an result that is unexpected.
+#todo[Image of disbalanced shots]
 
 == How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?
-How much does the performance improve if only detecting an anomaly or not?
-How does it compare to PatchCore and EfficientAD?
+_How much does the performance improve if only detecting an anomaly or not?
+How does it compare to PatchCore and EfficientAD?_
 
 == Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?

implementation.typ

@@ -6,7 +6,7 @@
 = Implementation
 The three methods described (ResNet50, CAML, P>M>F) were implemented in a Jupyter notebook and compared to each other.
 
-== Experiments
+== Experiments <experiments>
 For all of the three methods we test the following use-cases:#todo[maybe write more to each test]
 - Detection of anomaly class (1,3,5 shots)
 - 2 Way classification (1,3,5 shots)
@@ -20,7 +20,7 @@ Those experiments were conducted on the MVTEC AD dataset on the bottle and cable
 == Experiment Setup
 All the experiments were done on the bottle and cable classes of the MVTEC AD dataset.
 The correspoinding number of shots were randomly selected from the dataset.
-The rest of the images were used to test the model and measure the accuracy.
+The rest of the images was used to test the model and measure the accuracy.
 #todo[Maybe add real number of samples per classes]
 
 == ResNet50
@@ -86,7 +86,7 @@ In the following diagram the ResNet50 architecture is visualized and the cut-poi
 After creating the embeddings for the support and query set the euclidean distance is calculated.
 The class with the smallest distance is chosen as the predicted class.
 
-=== Results
+=== Results <resnet50perf>
 This method performed better than expected wich such a simple method.
 As in @resnet50bottleperfa with a normal 5 shot / 4 way classification the model achieved an accuracy of 75%.
 When detecting only if there occured an anomaly or not the performance is significantly better and peaks at 81% with 5 shots / 2 ways.
@@ -131,10 +131,25 @@ but this is expected as the cable class consists of 8 faulty classes.
 
 == P>M>F
 === Approach
+For P>M>F the pretrained model weights from the original paper were used.
+As backbone feature extractor a DINO model is used, which is pre-trained by facebook.
+This is a vision transformer with a patch size of 16 and 12 attention heads learned in a self-supervised fashion.
+This feature extractor was meta-trained with 10 public image dasets #footnote[ImageNet-1k, Omniglot, FGVC-
+Aircraft, CUB-200-2011, Describable Textures, QuickDraw,
+FGVCx Fungi, VGG Flower, Traffic Signs and MSCOCO~#cite(<pmfpaper>)]
+ of diverse domains  by the authors of the original paper.#cite(<pmfpaper>)
+
+Finally, this model is finetuned with the support set of every test iteration.
+Everytime the support set changes we need to finetune the model again.
+In a real world scenario this should not be the case because the support set is fixed and only the query set changes.
+
 === 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.
-#todo[write bit more here]
+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]
+
+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.
 
 #subpar.grid(
   figure(image("rsc/pmf/P>M>F-bottle.png"), caption: [