README.md

metadata

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

pip install -r requirements.txt
python app.py