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title: NILMbench
emoji: ⚡
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 4.44.0
python_version: "3.12"
app_file: app.py
pinned: false
license: mit
short_description: High-frequency NILM disaggregation on UK-DALE.
---
# NILMbench demo
This Space runs the FaustineCNN baseline trained on UK-DALE House 1 against a
single 6-second 16 kHz voltage/current frame from House 2.
* Upload a ``(2, 96000)`` float32 NumPy file, or pick one of the built-in
example frames.
* The model returns a per-category predicted power vector, post-processed with
the recall-constrained validation cutoffs from the paper.
The demo intentionally exposes a single frame at a time so the result fits in
one screen. For full benchmark scoring use the ``nilmbench`` CLI on the
companion GitHub repo.
## Files
| File | Purpose |
| ----------------- | -------------------------------------------------------- |
| `app.py` | Gradio entry point |
| `requirements.txt`| Pinned runtime dependencies |
| `examples/` | Built-in V/I frames and their ground-truth labels |
| `model/` | FaustineCNN checkpoint + class names + cutoffs |
## Local development
```bash
pip install -r requirements.txt
python app.py
```
|