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README.md

@@ -163,10 +163,17 @@ test_split    = ds["test"]       # 212 images — evaluation query set
 #   subdataset  — one of: BBBC, BCCD, LIVECell, NIH-3T3
 #   objects     — dict with keys:
 #                   bbox     : list of [x_min, y_min, width, height]  (COCO format, float32)
-#                   category : list of str  (class name, one of 10 classes)
+#                   category : list of int  (ClassLabel index; decode with int2str)
+
+# Decode category indices to class name strings
+label_feature = ds["example"].features["objects"].feature["category"]
+label_feature.int2str(3)   # → "Red Blood Cells"
+
+row = example_split[0]
+names = [label_feature.int2str(i) for i in row["objects"]["category"]]
 ```
 
-> **Note on bbox format:** The Parquet files store bboxes in COCO format `[x_min, y_min, width, height]` as float32, and `category` as a `ClassLabel`. 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 — 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).
 
 ## Dataset Statistics