Update README.md

README.md

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+---
 license: mit
 library_name: pytorch
 tags:
@@ -41,16 +42,17 @@ A small convolutional neural network, written and trained from scratch in PyTorc
 ## Results (60 held-out test spectrograms)
 
 - Test accuracy: 86.67%
-- Weighted F1: 0.87 (precision 0.87, recall 0.86%)
-- Per-class one-vs-rest ROC-AUC: helicopter 1.00, drone 0.97, background 0.96
+- Weighted F1: 0.87 (precision 0.87, recall 0.87)
+- Per-class one-vs-rest ROC-AUC: helicopter 0.99, drone 0.96, background 0.94
 
 ## Limitations
 
 - Small dataset of 297 recordings and a small test set (60 images) so these figures are indicative rather than precise.
 - Training loss is very low while validation accuracy is lower, which suggests overfitting. No data augmentation or regularisation was used.
-- The split is random at image level. [State whether all spectrograms come from separate recordings. If some share a source recording, say so: near-duplicate clips in train and test would inflate the scores, and splitting by recording is the planned next step.]
+- Background clips are sometimes misclassified as helicopter.
+- The split is random at image level.
 - Not tested on recordings from new environments, microphones or noise conditions.
-- A baseline model for demonstration. Not for safety-critical use.
+- This is a baseline model for problem-solving demonstration. Not for safety-critical use.
 
 ## Usage