add result images for each method

implementation.typ

@@ -1,6 +1,7 @@
 #import "@preview/fletcher:0.5.3" as fletcher: diagram, node, edge
 #import fletcher.shapes: rect, diamond
 #import "utils.typ": todo
+#import "@preview/subpar:0.1.1"
 
 = Implementation
 The three methods described (ResNet50, CAML, P>M>F) were implemented in a Jupyter notebook and compared to each other.
@@ -8,16 +9,19 @@ The three methods described (ResNet50, CAML, P>M>F) were implemented in a Jupyte
 == 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)
-- Inbalanced target class prediction (5,10,15,30 good shots, 5 bad shots)
 - 2 Way classification (1,3,5 shots)
-- Inbalanced 2 Way classification (5,10,15,30 good shots, 5 bad shots)
 - Detect only anomaly classes (1,3,5 shots)
+- Inbalanced 2 Way classification (5,10,15,30 good shots, 5 bad shots)
+- Inbalanced target class prediction (5,10,15,30 good shots, 5 bad shots)
 
 Those experiments were conducted on the MVTEC AD dataset on the bottle and cable classes.
 
 
 == Experiment Setup
-#todo[Setup of experiments, which classes used, nr of samples]
+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.
+#todo[Maybe add real number of samples per classes]
 
 == ResNet50
 === Approach
@@ -83,13 +87,78 @@ After creating the embeddings for the support and query set the euclidean distan
 The class with the smallest distance is chosen as the predicted class.
 
 === Results
-This method perofrmed better than expected wich such a simple method.
+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.
+Interestintly the model performed slightly better with fewer shots in this case.
+Moreover in @resnet50bottleperfa, the detection of the anomaly class only (3 way) shows a similar pattern as the normal 4 way classification.
+The more shots the better the performance and it peaks at around 88% accuracy with 5 shots.
 
-#todo[Add images of graphs with ResNet50 stuff only]
+In @resnet50bottleperfb the model was tested with inbalanced class distributions.
+With [5,10,15,30] good shots and 5 bad shots the model performed worse than with balanced classes.
+The more good shots the worse the performance.
+The only exception is the faulty or not detection (2 way) where the model peaked at 15 good shots with 83% accuracy.
+
+#subpar.grid(
+  figure(image("rsc/resnet/ResNet50-bottle.png"), caption: [
+    Normal [1,3,5] shots
+  ]), <resnet50bottleperfa>,
+  figure(image("rsc/resnet/ResNet50-bottle-inbalanced.png"), caption: [
+    Inbalanced [5,10,15,30] shots
+  ]), <resnet50bottleperfb>,
+  columns: (1fr, 1fr),
+  caption: [ResNet50 performance on bottle class],
+  label: <resnet50bottleperf>,
+)
+
+The same experiments were conducted on the cable class and the results are shown in @resnet50cableperfa and @resnet50cableperfb.
+The results are very similar to the bottle class.
+Generally the more shots the better the accuracy.
+But the overall reached max accuracy is lower than on the bottle class,
+but this is expected as the cable class consists of 8 faulty classes.
+
+#subpar.grid(
+  figure(image("rsc/resnet/ResNet50-cable.png"), caption: [
+    Normal [1,3,5] shots
+  ]), <resnet50cableperfa>,
+  figure(image("rsc/resnet/ResNet50-cable-inbalanced.png"), caption: [
+    Inbalanced [5,10,15,30] shots
+  ]), <resnet50cableperfb>,
+  columns: (1fr, 1fr),
+  caption: [ResNet50 performance on cable class],
+  label: <resnet50cableperf>,
+)
 
 == P>M>F
 === Approach
 === 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]
+
+#subpar.grid(
+  figure(image("rsc/pmf/P>M>F-bottle.png"), caption: [
+    Normal [1,3,5] shots
+  ]), <pmfbottleperfa>,
+  figure(image("rsc/pmf/P>M>F-bottle-inbalanced.png"), caption: [
+    Inbalanced [5,10,15,30] shots
+  ]), <pmfbottleperfb>,
+  columns: (1fr, 1fr),
+  caption: [P>M>F performance on bottle class],
+  label: <pmfbottleperf>,
+)
+
+#subpar.grid(
+  figure(image("rsc/pmf/P>M>F-cable.png"), caption: [
+    Normal [1,3,5] shots
+  ]), <pmfcableperfa>,
+  figure(image("rsc/pmf/P>M>F-cable-inbalanced.png"), caption: [
+    Inbalanced [5,10,15,30] shots
+  ]), <pmfcableperfb>,
+  columns: (1fr, 1fr),
+  caption: [P>M>F performance on cable class],
+  label: <pmfcableperf>,
+)
 
 == CAML
 === Approach
@@ -106,4 +175,18 @@ This might be caused by the fact that the model was not fine-tuned for any indus
 The model was trained on a large number of general purpose images and is not fine-tuned at all.
 It might not handle very similar images well.
 
-#todo[Add images of graphs with CAML stuff only]
+Compared the the other two methods CAML performed poorly in almost all experiments.
+The normal few-shot classification reached only 40% accuracy in @camlperfa at best.
+The only test it did surprisingly well was the detection of the anomaly class for the cable class in @camlperfb were it reached almost 60% accuracy.
+
+#subpar.grid(
+  figure(image("rsc/caml/CAML-bottle.png"), caption: [
+    Normal [1,3,5] shots - Bottle
+  ]), <camlperfa>,
+  figure(image("rsc/caml/CAML-cable.png"), caption: [
+    Normal [1,3,5] shots - Cable
+  ]), <camlperfb>,
+  columns: (1fr, 1fr),
+  caption: [CAML performance],
+  label: <camlperf>,
+)