AnyCalib — MLX weights
Converted weights for mlx-swift-AnyCalib, a Swift/MLX port of AnyCalib (Tirado-Garín & Civera, ICCV 2025): single-view camera calibration with a camera model chosen after the network runs.
These are not new weights. They are the official AnyCalib checkpoints in a different container.
What was changed
- Repacked from PyTorch
.ptintosafetensors. - Convolution tensors transposed
(out, in, kH, kW) → (out, kH, kW, in)for MLX's native NHWC layout. That same rule also reshapes the(1, 3, 1, 1)ImageNet mean/std buffers to(1, 1, 1, 3). - Optionally cast to
float16; the default build isfloat32.
No weight values are altered beyond that optional cast. The port is verified stage by stage against the PyTorch reference — on MLX's CPU backend every stage matches to within float32's own noise floor (ray field within 0.0003°).
Layout
One subdirectory per pretrained variant, each holding config.json and
weights.safetensors:
anycalib_dist/
anycalib_edit/
anycalib_gen/
anycalib_pinhole/
The variants differ only in training imagery: pinhole (perspective only),
gen (perspective + distorted), dist (distorted + strongly distorted),
edit (stretched and cropped perspective).
Usage
anycalib calibrate photo.jpg --cam pinhole --repo mnmly/anycalib-mlx
The Swift loader resolves these through the shared Hugging Face cache, so a copy
pulled by huggingface_hub is reused and vice versa.
License and attribution
Apache 2.0, inherited from upstream. See LICENSE and NOTICE in this repo.
Original work © Javier Tirado-Garín and Javier Civera, I3A, University of Zaragoza. The backbone is DINOv2, © Meta Platforms, Inc., also Apache 2.0.
@InProceedings{tirado2025anycalib,
author={Javier Tirado-Gar{\'i}n and Javier Civera},
title={{AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration}},
booktitle={ICCV},
year={2025}
}