Add prediction banner (LFS) to model card; remove citation

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -13,6 +13,8 @@ pipeline_tag: image-segmentation
 
 # Historical Map Semantic Segmentation — Ensemble Checkpoints
 
+![Input RGB strip and our ensemble's predictions on a region of map2](prediction_strip.png)
+
 Three U-Net + CBAM (EfficientNet-B5 encoder) checkpoints used as a 3-way
 probability-averaging ensemble for 7-class semantic segmentation of historical
 cartographic scans. Best Kaggle score: **0.77044** (`score = 0.6 · mIoU + 0.4 · macro-F1`).
@@ -42,7 +44,7 @@ from huggingface_hub import hf_hub_download
 
 # Pull one checkpoint
 ckpt_path = hf_hub_download(
-    repo_id="Noe-B/historical-map-segmentation",
+    repo_id="Noe-B/historic-map-semantic-segmentation",
     filename="map2_specialist/map2_specialist.pth",
 )
 
@@ -61,7 +63,3 @@ see the [`4_submit.py` script in the GitHub repo](https://github.com/VictorPache
 
 - **Input:** RGB tile, `(3, 768, 768)`, ImageNet-normalised (`mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`)
 - **Output:** logits, `(7, 768, 768)`; apply `sigmoid` then threshold (recommended `0.33`)
-
-## Citation
-
-Course: *Research Topics in Cartography*, ETH Zürich — Task 1 (2026).