docs: update README file to include initial setup

README.md

@@ -2,4 +2,81 @@
 license: cc-by-nc-sa-4.0
 language:
 - en
----
+library_name: sam2
+pipeline_tag: image-segmentation
+tags:
+- whole-slide-imaging
+- histopathology
+- tissue-segmentation
+- sam2
+---
+
+# AtlasPatch: Whole-Slide Image Tissue Segmentation
+
+Segmentation model for whole-slide image (WSI) thumbnails, built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail (longest side clamped to 1024 px, internally resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails. AtlasPatch codebase (WSI preprocessing & tooling): https://github.com/AtlasAnalyticsLab/SlideProcessor
+
+## Quickstart
+
+Install dependencies:
+
+```bash
+pip install atlas-patch
+```
+
+Recommended: use the same components we ship in AtlasPatch/SlideProcessor. The segmentation service will (a) load your WSI with the registered backend, (b) build a 1.25× power thumbnail, (c) resize it to 1024×1024, (d) run SAM2 with a full-frame box, and (e) return a mask aligned to the thumbnail.
+
+```python
+import numpy as np
+import torch
+from pathlib import Path
+from PIL import Image
+from importlib.resources import files
+
+from slide_processor.core.config import SegmentationConfig
+from slide_processor.services.segmentation import SAM2SegmentationService
+from slide_processor.core.wsi import WSIFactory
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 1) Config: packaged SAM2 Hiera-T config; leave checkpoint_path=None to auto-download from HF.
+cfg_path = Path(files("slide_processor.configs") / "sam2.1_hiera_t.yaml")
+seg_cfg = SegmentationConfig(
+    checkpoint_path=None,          # downloads Atlas-Patch/model.pth from Hugging Face
+    config_path=cfg_path,
+    device=str(device),
+    batch_size=1,
+    thumbnail_power=1.25,
+    thumbnail_max=1024,
+    mask_threshold=0.0,
+)
+segmenter = SAM2SegmentationService(seg_cfg)
+
+# 2) Load a WSI and segment the thumbnail.
+wsi = WSIFactory.load("slide.svs")  # backend auto-detected (e.g., openslide)
+mask = segmenter.segment_thumbnail(wsi)  # mask.data matches the thumbnail size
+
+# 3) Save the mask.
+mask_img = Image.fromarray((mask.data > 0).astype(np.uint8) * 255)
+mask_img.save("thumbnail_mask.png")
+```
+
+## Preparing the Thumbnail
+
+AtlasPatch generates thumbnails at **1.25× objective power** (power-based downsampling) and then clamps the longest side to **1024 px**. Using the same helper the library uses:
+
+```python
+from slide_processor.core.wsi import WSIFactory
+
+wsi = WSIFactory.load("slide.svs")
+thumb = wsi.get_thumbnail_at_power(power=1.25, interpolation="optimise")
+thumb.thumbnail((1024, 1024))  # in-place resize to 1024×1024
+thumb.save("thumbnail.png")
+```
+
+## License and Commercial Use
+
+This model is released under **CC-BY-NC-SA-4.0**, which strictly disallows commercial use of the model weights or any derivative works. Commercialization includes selling the model, offering it as a paid service, using it inside commercial products, or distributing modified versions for commercial gain. Non-commercial research, experimentation, educational use, and use by academic or non-profit organizations is permitted under the license terms. If you need commercial rights, please contact the authors to obtain a separate commercial license. See the LICENSE file in this repository for full terms.
+
+## Citation
+
+If you use this model, please cite SAM2 and the AtlasPatch project. A formal paper is forthcoming.