Refactor output schema creation to define features before processing dataset

detect-objects.py

@@ -328,7 +328,18 @@ def main():
         logger.error("Ensure the model exists and you have access permissions")
         sys.exit(1)
 
-    # Process dataset
+    # Define output schema before processing
+    logger.info("📊 Creating output schema...")
+    new_features = dataset.features.copy()
+    new_features["objects"] = Sequence(
+        {
+            "bbox": Sequence(Value("float32"), length=4),
+            "category": ClassLabel(names=[class_name]),
+            "score": Value("float32"),
+        }
+    )
+
+    # Process dataset with explicit output features
     logger.info("🔍 Processing images...")
     processed_dataset = dataset.map(
         lambda batch: process_batch(
@@ -342,23 +353,10 @@ def main():
         ),
         batched=True,
         batch_size=args.batch_size,
+        features=new_features,
         desc="Detecting objects",
     )
 
-    # Create dynamic features with ClassLabel
-    logger.info("📊 Creating output schema...")
-    new_features = processed_dataset.features.copy()
-    new_features["objects"] = Sequence(
-        {
-            "bbox": Sequence(Value("float32"), length=4),
-            "category": ClassLabel(names=[class_name]),
-            "score": Value("float32"),
-        }
-    )
-
-    # Cast to proper types
-    processed_dataset = processed_dataset.cast(new_features)
-
     # Calculate statistics
     total_detections = sum(len(objs) for objs in processed_dataset["objects"])
     images_with_detections = sum(len(objs) > 0 for objs in processed_dataset["objects"])