BOPBTL-scratch-detection-fp32-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), 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 — 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.