add stuff for CAML

implementation.typ

@@ -15,6 +15,10 @@ For all of the three methods we test the following use-cases:#todo[maybe write m
 
 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]
+
 == ResNet50
 === Approach
 The simplest approach is to use a pre-trained ResNet50 model as a feature extractor.
@@ -79,23 +83,27 @@ 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.
 
-
-== CAML
+#todo[Add images of graphs with ResNet50 stuff only]
 
 == P>M>F
+=== Approach
+=== Results
 
-== Experiment Setup
-% todo
-todo setup of experiments, which classes used, nr of samples
-kinds of experiments which lead to graphs
+== CAML
+=== Approach
+For the CAML implementation the pretrained model weights from the original paper were used.
+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
+It consists of 24 Layers with 16 Attention-heads and a hidden dimension of 1024 and output MLP size of 4096.
+This transformer was trained on a huge number of images as described in @CAML.
 
-== Jupyter
+=== Results
+The results were not as good as expeced.
+This might be caused by the fact that the model was not fine-tuned for any industrial dataset domain.
+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.
 
-To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
-This helps to choose which of the methods performs the best and which one to use in the final Dagster pipeline.
-A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet-18.
-
-Moreover, the Dataset was manually imported with the help of a custom torch dataloader and preprocessed with random augmentations.
-After each loop iteration the Area Under the Curve (AUC) was calculated over the validation set to get a performance measure.
-All those AUC were visualized in a line plot, see section~\ref{sec:experimental-results} for the results.
+#todo[Add images of graphs with CAML stuff only]