Update README.md

README.md

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----
 license: mit
+library_name: pytorch
+tags:
+  - audio-classification
+  - spectrogram
+  - cnn
+  - pytorch
 ---
+
+# Drone / Helicopter / Background Audio Classifier
+
+A small convolutional neural network, written and trained from scratch in PyTorch, that classifies spectrogram images of audio into three classes: **background**, **helicopter** and **drone**. Developed from work at Ministry of Defence hackathons (Sandhurst) and deployed independently in 2026.
+
+## Classes (output index order)
+
+| Index | Class |
+|---|---|
+| 0 | background |
+| 1 | helicopter |
+| 2 | drone |
+
+## Model
+
+- **Input:** single-channel (grayscale) spectrogram image, 480 x 640 pixels, pixel values scaled to 0-1.
+- **Architecture:** two convolutional layers (64 then 128 filters, 3x3 kernels), each followed by ReLU and 2x2 max pooling, then one linear layer to 3 classes. Output is log-probabilities (`log_softmax`), so apply `exp` to get probabilities.
+- **Code:** the architecture is in `model.py` (`CnnNet`) and a minimal inference example is in `predict.py`.
+- **Weights:** `weightsfilefromspectrogram.pth` (PyTorch `state_dict`).
+
+## Data
+
+- 297 spectrogram images: 101 background, 99 helicopter, 97 drone.
+- Spectrograms were generated from [source audio: where the recordings came from, and the tool or settings used to convert them].
+- Random 60/20/20 train/validation/test split (`random_state=42`), about 178 / 59 / 60 images.
+
+## Training
+
+- Framework: PyTorch, trained on Google Colab.
+- Loss: negative log-likelihood. Optimiser: SGD, learning rate 0.001, momentum 0.5, batch size 4, 115 epochs.
+- The checkpoint with the best validation accuracy was kept.
+
+## Results (60 held-out test spectrograms)
+
+- Test accuracy: [xx.x]%
+- Weighted F1: [0.xx] (precision [0.xx], recall [0.xx])
+- Per-class one-vs-rest ROC-AUC: helicopter 1.00, drone 0.97, background 0.96
+
+## Limitations
+
+- Small dataset, 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.]
+- Not tested on recordings from new environments, microphones or noise conditions.
+- A baseline model for demonstration. Not for safety-critical use.
+
+## Usage
+
+```python
+from predict import predict
+print(predict("example_spectrogram.png"))
+```