Release v0.25.0

0.25.0 (2025-12-11)

Adds full support for non-square image sizes with CLI parsing, data/augmentation/mappers/processors updates, inference/export benchmarking changes, and docs/examples; also updates AutoDataset.get_split to accept DatasetAugmentations.

• Image size handling (core):
  • Make ModelInfo.im_size accept int | (H, W); propagate through TrainerArgs, processors, mappers, and ports.
  • Update processors (DETR, RTMO, MaskFormer, BisenetFormer, Classification) to accept tuple sizes and resize accordingly.
• Adjust inference runtimes (ONNX, TorchScript, base model) warmup/benchmark to support non-square and log proper size; set latency im_size using height.
• CLI:
  • Add parse_im_size to accept "640", "640,480", or "640x480".
  • Change train, val, and export --im-size option to string, parse to int | (H, W) and pass through.
• Data pipeline:
  • DatasetAugmentations.resolution now int | (H, W); non-square uses direct Resize, square keeps ResizeShortestEdge; adjust crop to use absolute size.
  • get_default_by_task and all call sites now pass/return DatasetAugmentations (not raw list).
  • AutoDataset.get_split(augs=...) now expects DatasetAugmentations and forwards both augs and resolution; remove .get_augmentations() usage across code/tests/tutorials.
  • Mappers carry resolution; MapDataset exposes resolution property.
• Export/Training:
  • export_command and model export accept tuple sizes; set model_info.im_size to the provided resolution.
  • After training, always reload best model/info and set model/processor to eval.
• Docs:
  • Update README and docs (CLI, concepts, inference, training) with non-square examples and usage notes.

What's Changed

• feat: enhance image size handling across the codebase by @CuriousDolphin in #167

Full Changelog: v0.24.0...v0.25.0