Update README.md

README.md

@@ -47,16 +47,17 @@ A small convolutional neural network, written and trained from scratch in PyTorc
 
 ## Limitations
 
-- Small dataset of 297 recordings and a small test set (60 images) so these figures are indicative rather than precise.
+- Small dataset of 297 spectrograms 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.
 - Background clips are sometimes misclassified as helicopter.
-- The split is random at image level.
+- The split is random at image level. If several spectrograms come from the same recording, near-duplicates could appear in both train and test and inflate the scores; splitting by recording is a planned improvement.
 - Not tested on recordings from new environments, microphones or noise conditions.
 - This is a baseline model for problem-solving demonstration. Not for safety-critical use.
 
 ## Usage
 
 ```python
-from predict import predict
-print(predict("example_spectrogram.png"))
+Download model.py, predict.py and weightsfilefromspectrogram.pth into one folder, install the dependencies, then run:
+
+pip install torch pillow numpy
 ```