fix some todos and spelling errors

main.typ

@@ -57,7 +57,8 @@
     FSL offers a promising alternative by enabling models to generalize effectively from minimal samples, thus reducing training time and adaptation overhead.
 
     The study evaluates three FSL methods—ResNet50, P>M>F, and CAML—using the MVTec AD dataset.
-    Experiments focus on tasks such as anomaly detection, class imbalance handling, and comparison of distance metrics.
+    Experiments focus on tasks such as anomaly detection, class imbalance handling, //and comparison of distance metrics.
+    and anomaly type classification.
     Results indicate that while FSL methods trail behind state-of-the-art algorithms in detecting anomalies, they excel in classifying anomaly types, showcasing potential in scenarios requiring detailed defect identification.
     Among the tested approaches, P>M>F emerged as the most robust, demonstrating superior accuracy across various settings.