---
license: mit
tags:
  - nilm
  - energy-disaggregation
  - uk-dale
  - pytorch
library_name: nilmbench
datasets:
  - Pybunny/nilmbench-ukdale
language:
  - en
---

# FaustineCNN — NILMbench baseline

FaustineCNN trained on UK-DALE House 1 (sparse 6-second 16 kHz V/I frames) and
evaluated on the dense House 2 set of 60,000 frames. This is the best-scoring
baseline from the NILMbench paper (`MJ_{20W} = 0.504` raw,
`0.559` with the recall-constrained cutoffs in `cutoffs.json`).

## Files

| File              | Purpose                                                              |
| ----------------- | -------------------------------------------------------------------- |
| `faustine_best.pt`| PyTorch state-dict, loadable with `nilmbench.models.FaustineCNN`.    |
| `cutoffs.json`    | Recall-constrained per-class cutoffs `c_k` (calibrated on House 1).  |
| `classes.json`    | Ordered category names (length 7).                                   |

## Quick start

```python
import torch, json
from huggingface_hub import hf_hub_download
from nilmbench.models import FaustineCNN

ckpt = hf_hub_download("Pybunny/nilmbench-faustine", "faustine_best.pt")
classes = json.load(open(hf_hub_download("Pybunny/nilmbench-faustine", "classes.json")))

model = FaustineCNN(n_categories=len(classes))
model.load_state_dict(torch.load(ckpt, map_location="cpu"))
model.eval()
```

## Citation

NILMbench paper (2026). See https://github.com/Saharmgh/NILMbench for the
full code and reproducible-figure scripts.

## License

MIT.