Swin Transformer

Install

Swin 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("LibreSwint-cls.pt")result = model(SAMPLE_IMAGE, save=True) probs = result.probsprint(probs.top1, probs.top1conf)print(probs.top5, probs.top5conf)

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

A classifier returns result.probs instead of result.boxes: top1 and top5 give class indices, top1conf and top5conf give their confidences. Every size is fixed to a 224px input, because the final attention stage is built for that resolution; predict, validate and export all raise if you pass a different imgsz. See prediction for sources, streaming and result handling.

Variants

Four sizes, tiny through large, built from the same shifted-window tower and differing in embedding width and stage depth. Large is pretrained on ImageNet-22k and fine-tuned on ImageNet-1k; the other three are trained on ImageNet-1k directly. LibreYOLO ships this family inference-only: prediction, ImageNet-style top-1/top-5 validation and export are supported, and the upstream ImageNet training recipe is not implemented.

Validate

val() runs against an ImageFolder-style split (a directory with train/ and val/ subfolders, one folder per class) and returns top-1 and top-5 accuracy.

from libreyolo import LibreYOLO model = LibreYOLO("LibreSwint-cls.pt") # data is a directory root with train/ and val/ class-folder splits# (ImageFolder layout), not a dataset YAML.metrics = model.val(data="imagenet-1k/") print(metrics["metrics/accuracy_top1"])print(metrics["metrics/accuracy_top5"])

libreyolo val model=LibreSwint-cls.pt data=imagenet-1k/

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. Export lists the arguments every format accepts and the extras a few of them add.

from libreyolo import LibreYOLO model = LibreYOLO("LibreSwint-cls.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreSwint-cls.pt format=onnxlibreyolo export model=LibreSwint-cls.pt format=tensorrt half=True

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("LibreSwint-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@inproceedings{liu2021Swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}