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README.md

@@ -165,12 +165,19 @@ test_split    = ds["test"]       # 212 images — evaluation query set
 #                   bbox     : list of [x_min, y_min, width, height]  (COCO format, float32)
 #                   category : list of int  (ClassLabel index; decode with int2str)
 
+row = test_split[0]
+
+# Image (PIL.Image)
+image = row["image"]
+
 # Decode category indices to class name strings
-label_feature = ds["example"].features["objects"].feature["category"]
-label_feature.int2str(3)   # → "Red Blood Cells"
+label_feature = ds["test"].features["objects"].feature["category"]
 
-row = example_split[0]
-names = [label_feature.int2str(i) for i in row["objects"]["category"]]
+# 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)
+    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).