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README.md

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+---
+license: cc-by-nc-4.0
+tags: [medical-imaging, segmentation, benchmark]
+---
+
+# GenSeg-Baselines
+
+Baseline benchmark for 2D medical image segmentation: **8 methods x 10 datasets x 3 seeds/folds, 7 metrics**.
+Companion to the [GenSegDataset](https://huggingface.co/datasets/MaybeRichard/GenSegDataset).
+
+**Methods:** UNet, UNet++, DeepLabV3+ (ResNet-50/ImageNet), Attention-UNet (scratch),
+TransUNet (R50-ViT-B/16), Swin-UNet (Swin-Tiny), nnU-Net v2 (250ep), U-Mamba (UMambaBot, 100ep).
+
+**Datasets:** cvc_clinicdb, kvasir_seg, fives, busi, refuge2, acdc, idridd, pannuke, isic2018, kits19.
+
+**Metrics:** Dice, IoU, HD95, ASSD, Sensitivity, Specificity, Precision (+ efficiency).
+
+## Layout
+- `code/` - baseline framework (train/test/aggregate), scripts, conda envs. *(Generative SegGen code excluded.)*
+- `results/` - per-run `metrics.json` + aggregated `summary.{html,csv,md,tex}` + `efficiency.md`.
+- `weights/` - curated checkpoints: best seed per (dataset, arch) for framework; best fold for nnU-Net / U-Mamba.
+
+## Note
+These are the **256-px baseline** (confirmed). A resolution-fair re-evaluation (conv methods retrained at a
+higher per-dataset resolution; all methods scored at a common R so HD95 is comparable) is in progress and
+will be added later.