Update README.md

README.md

@@ -25,13 +25,25 @@ generalizes to street-level photos, where orientation matters and the classes ov
 
 | Metric | Value |
 |---|---|
-| Test accuracy (seg_test, 3,000 images) | **XX.X%** |
-| Macro F1 | **0.XXX** |
-| Best validation accuracy | **XX.X%** |
-| Parameters | **~1.21M** |
-
-Main confusions: **buildings ↔ street** and **glacier ↔ mountain ↔ sea** (snow, rock and water look alike).
-Several of the "confident mistakes" are actually labeling errors in the dataset.
+| Test accuracy (seg_test, 3,000 images) | **89.87%** |
+| Macro F1 | **0.900** |
+| Best validation accuracy | **90.98%** |
+| Parameters | **1,206,886** |
+
+| Class | Precision | Recall | F1 | Test images |
+|---|---|---|---|---|
+| buildings | 0.913 | 0.860 | 0.886 | 437 |
+| forest | 0.979 | 0.985 | 0.982 | 474 |
+| glacier | 0.846 | 0.866 | 0.856 | 553 |
+| mountain | 0.876 | 0.830 | 0.852 | 525 |
+| sea | 0.895 | 0.937 | 0.916 | 510 |
+| street | 0.897 | 0.918 | 0.907 | 501 |
+
+**Where it fails:** mountain → glacier (65), buildings → street (46), glacier → mountain (46),
+street → buildings (29). Almost all errors come from two pairs: snow-covered peaks vs. glaciers,
+and urban scenes where a photo of a street is also a photo of buildings. Forest is near-perfect
+(98% F1) because its texture and color are unique. Several high-confidence "mistakes" are
+labeling errors in the dataset itself.
 
 ![Confusion matrix](images/11_confusion_matrix.png)
 ![Per-class accuracy](images/12_per_class_accuracy.png)