fix more comma errors

implementation.typ

@@ -92,7 +92,7 @@ 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 <resnet50perf>
-This method performed better than expected wich such a simple method.
+This method performed better than expected with such a simple method.
 As in @resnet50bottleperfa with a normal 5 shot / 4 way classification the model achieved an accuracy of 75%.
 When detecting if there occured an anomaly or not only 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.
@@ -136,7 +136,7 @@ but this is expected as the cable class consists of 8 faulty classes.
 
 == P>M>F
 === Approach
-For P>M>F I used the pretrained model weights from the original paper.
+For P>M>F, I used the pretrained model weights from the original paper.
 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-
@@ -144,7 +144,7 @@ Aircraft, CUB-200-2011, Describable Textures, QuickDraw,
 FGVCx Fungi, VGG Flower, Traffic Signs and MSCOCO~@pmfpaper]
  of diverse domains  by the authors of the original paper.~@pmfpaper
 
-Finally, this model is finetuned with the support set of every test iteration.
+Finally, this model is fine-tuned with the support set of every test iteration.
 Every time 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.
 
@@ -196,12 +196,12 @@ This transformer was trained on a huge number of images as described in @CAML.
 
 === 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.
+This might be because 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.
 Moreover, it was not fine-tuned on the support set similar to the P>M>F method, which could have a huge impact on performance.
 It might also not handle very similar images well.
 
-Compared the the other two methods, CAML performed poorly in almost all experiments.
+Compared to 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.