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mnmly/
scalelsd-mlx

mlx

safetensors

mlx-swift

line-segment-detection

wireframe-parsing

image-feature-extraction

cherubicxn/scalelsd

finetune

apache-2.0

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ScaleLSD — MLX weights

MLX-format weights for ScaleLSD, converted for use with mlx-swift-ScaleLSD on Apple Silicon.

These are converted redistributions of the original checkpoints published at cherubicxn/scalelsd. No retraining or fine-tuning was performed — the numerical content of the network is unchanged.

folder	LayerScale	parameters	source checkpoint
`scalelsd-vitbase-v1/`	no	122,525,833	`scalelsd-vitbase-v1-train-sa1b.pt`
`scalelsd-vitbase-v2/`	yes	122,544,265	`scalelsd-vitbase-v2-train-sa1b.pt`

Each folder holds config.json + model.safetensors. Upstream recommends v2 by default.

Usage

swift

import MLXScaleLSD

// Downloads from this repo on first use, then caches locally.
let directory = try await ModelStore.download(.v2)
let session = try ScaleLSDSession.load(directory: directory)

let image = try ScaleLSDSession.loadImage(at: imageURL)
let result = try session.detect(image)
for segment in result.segments(minimumScore: 10) {
    print(segment.x1, segment.y1, segment.x2, segment.y2, segment.score)
}

Or from the command line:

scalelsd detect -m <directory> -i image.jpg -e png --save-to out/

What was changed in conversion

The original checkpoints are PyTorch pickles, which MLX cannot read. Scripts/convert.py in the Swift repo performs a format conversion plus several inference-only graph simplifications, each of which is numerically equivalent (verified to ~2e-6 relative against the PyTorch reference):

Weight standardisation baked in. timm's StdConv2dSame re-standardises its weight on every forward pass; inference weights are frozen, so the standardised tensor is stored directly. (Note: the hybrid ViT uses eps=1e-8, not the class default 1e-6.)
conv + BatchNorm folded. The 16 conv(bias=False) -> BatchNorm2d pairs in DPT's ResidualConvUnit_custom collapse into single biased convolutions.
nn.Sequential indices renamed to named submodules, so keys read structurally.
Conv weights transposed from PyTorch (O, I, kH, kW) to MLX (O, kH, kW, I).
The 1000-class ImageNet classifier head dropped — ScaleLSD never calls it.

Accuracy

Verified stage by stage against the PyTorch reference. The final 9-channel HAT field matches to 1.1e-05 (v1) / 1.4e-05 (v2) maximum relative error. End-to-end on assets/indoor.jpg:

	v1	v2
junctions matched within 0.01 px	512/512	511/512
segments matched within 0.01 px	1879/1880	1581/1590

Detections are not bit-exact by construction: the 512-junction cap and the nearest-junction assignment are discrete choices that a sub-noise perturbation can flip. See docs/PARITY.md.

Performance

Apple M5 Max, 512×512 input, Release build, median of 20 runs:

runtime	per image
mlx-swift (this port)	57 ms
PyTorch 2.13, MPS	88 ms
PyTorch 2.13, CPU	652 ms

License and attribution

Apache-2.0, inherited from the original checkpoints at cherubicxn/scalelsd. The upstream ScaleLSD source is MIT (Copyright © 2023 Nan Xue). Original work and all model credit belong to the ScaleLSD authors; this repository contributes only a format conversion.

bibtex

@inproceedings{ScaleLSD,
    title = {ScaleLSD: Scalable Deep Line Segment Detection Streamlined},
    author = {Zeran Ke and Bin Tan and Xianwei Zheng and Yujun Shen and Tianfu Wu and Nan Xue},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2025},
}

mnmly/scalelsd-mlx