| 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 | |
| ``` | |