SwinIR

Install

SwinIR needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreSwinIRm-restore.pt")result = model(SAMPLE_IMAGE, save=True) restored = result.restoredprint(restored.array.shape, restored.array.dtype)

libreyolo predict model=LibreSwinIRm-restore.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO model = LibreYOLO("LibreSwinIRl-restore.pt") # tile splits the forward pass into overlapping tiles and blends the# seams back together; tile_pad is the halo added around each tile# before it is cropped back out. Both are Python-only keyword# arguments, not CLI flags.result = model("large-photo.jpg", tile=512, tile_pad=16, save=True)

A restore result carries no boxes; result.restored is a dense (H, W, 3) uint8 RGB image, on a canvas 4x the input in each dimension. save=True writes that image directly rather than an annotated plot. The input is padded to a multiple of 8 rather than resized, so predict runs at the photo's own resolution; a source larger than memory allows can be split with tile and tile_pad, which blend the tile seams back together in the output. See prediction for sources, streaming and result handling.

Variants

Three sizes, all fixed at a 4x upscale. s is the official lightweight generator, with four residual Swin Transformer block (RSTB) stages and pixel-shuffle-direct upsampling. m and l are the real-world medium and large generators, with six and nine RSTB stages and a nearest-neighbor-plus- convolution upsampler built for real-world degradations rather than only bicubic downscaling.

Validate

val() measures PSNR and SSIM between the restored output and a clean target image, both computed in RGB on the original canvas with no border crop and no resizing. SSIM uses an 11x11 Gaussian window with sigma 1.5, averaged over the three color channels.

from libreyolo import LibreYOLO model = LibreYOLO("LibreSwinIRm-restore.pt")metrics = model.val(data="my-restore-dataset.yaml") print(metrics["metrics/PSNR"])print(metrics["metrics/SSIM"])

libreyolo val model=LibreSwinIRm-restore.pt data=my-restore-dataset.yaml

The dataset argument is a YAML pairing a directory of degraded input images with a directory of clean target images of matching resolution; see dataset formats for the exact keys.

Export

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. ExecuTorch and every format the matrix marks blocked are not available for this family; ONNX, TorchScript, TensorRT, OpenVINO and TFLite are. Export lists the arguments every format accepts and the extras a few of them add.

from libreyolo import LibreYOLO model = LibreYOLO("LibreSwinIRm-restore.pt") # imgsz defaults to a small internal patch size when omitted, not# your working resolution, so pass the size your deployment actually# feeds the model.model.export(format="onnx", imgsz=512)model.export(format="tensorrt", imgsz=512, half=True)

libreyolo export model=LibreSwinIRm-restore.pt format=onnx imgsz=512

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreSwinIRm-restore.onnx")result = model(SAMPLE_IMAGE) print(result.restored.array.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{liang2021swinir,
  title={SwinIR: Image Restoration Using Swin Transformer},
  author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
  journal={arXiv preprint arXiv:2108.10257},
  year={2021}
}