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README.md

@@ -170,17 +170,24 @@ row = test_split[0]
 # Image (PIL.Image)
 image = row["image"]
 
-# Decode category indices to class name strings
-label_feature = ds["test"].features["objects"]["category"].feature
-
-# Iterate over bounding boxes with labels
-for bbox, category_idx in zip(row["objects"]["bbox"], row["objects"]["category"]):
-    x_min, y_min, width, height = bbox   # COCO format (float32)
-    label = label_feature.int2str(category_idx)
+# Bounding boxes and category indices
+bboxes     = row["objects"]["bbox"]      # list of [x_min, y_min, width, height] (float32)
+categories = row["objects"]["category"]  # list of int (ClassLabel index)
+
+# Class names in ClassLabel index order (alphabetically sorted)
+CLASS_NAMES = [
+    "Gametocyte Cells", "Platelets", "Polygonal Cells", "Red Blood Cells",
+    "Ring Cells", "Round Cells", "Schizont Cells", "Spindle Cells",
+    "Trophozoite Cells", "White Blood Cells",
+]
+
+for bbox, cat_idx in zip(bboxes, categories):
+    x_min, y_min, width, height = bbox
+    label = CLASS_NAMES[cat_idx]
     print(f"{label}: [{x_min:.1f}, {y_min:.1f}, {width:.1f}, {height:.1f}]")
 ```
 
-> **Note on bbox format:** The Parquet files store bboxes in COCO format `[x_min, y_min, width, height]` as float32. `category` is stored as a `ClassLabel` integer index — use `int2str()` to get the string name. The raw `annotation.jsonl` files use `[[x_min, y_min], [x_max, y_max]]` (top-left / bottom-right pixel coordinates) — see [Annotation Format](#annotation-format).
+> **Note on bbox format:** The Parquet files store bboxes in COCO format `[x_min, y_min, width, height]` as float32. `category` is stored as a `ClassLabel` integer index. The raw `annotation.jsonl` files use `[[x_min, y_min], [x_max, y_max]]` (top-left / bottom-right pixel coordinates) — see [Annotation Format](#annotation-format).
 
 ## Dataset Statistics