---
license: mit
tags:
- mlx
- image-to-image
- scratch-detection
- photo-restoration
library_name: mlx
---

# BOPBTL-scratch-detection-fp32-mlx

The scratch/defect **detector** from Microsoft's *Bringing Old Photos Back to Life*
([microsoft/Bringing-Old-Photos-Back-to-Life](https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life)),
converted for MLX: grayscale photo in → per-pixel damage probability out.

- `UNet(in=1, out=1, depth=4, conv_num=2, wf=6, batch_norm, up_mode="upsample", antialiasing=True)`,
  37,626,177 params, fp32 (150.6 MB — the released 1.90 GiB checkpoint minus its Adam state).
- Keys are the original torch `state_dict` names (NCHW conv layout), including BatchNorm running
  stats and the BlurPool `filt` buffers — consumers transpose per their framework's convention.
- Reference behaviour: short-side-256 (/16-aligned) BICUBIC resize, grayscale, Normalize([.5],[.5]);
  `sigmoid(out) ≥ 0.4` is upstream's mask threshold.

**Swift consumer:** [`xocialize/mlx-bopbtl-swift`](https://github.com/xocialize/mlx-bopbtl-swift) —
parity-locked at **119.8 dB worst** vs fp32 PyTorch (and the reason its forward runs on the CPU
stream is documented there: a measured Metal fp32 conv2d divergence window, not a preference).

## License

MIT — the upstream LICENSE file, all source headers, and README §License (which extends MIT to
"the codes and the pretrained model" verbatim). The training dataset was never released; the model
cannot be retrained or audited.