Refactor output format to use dict-of-lists for object detections and update output schema accordingly

detect-objects.py

@@ -251,37 +251,57 @@ def process_batch(
 
     except Exception as e:
         logger.warning(f"⚠️  Failed to process batch: {e}")
-        # Return empty detections for all images in batch
-        return {"objects": [[] for _ in range(len(pil_images))]}
+        # Return empty detections for all images in batch (dict-of-lists format)
+        num_images = len(pil_images)
+        return {
+            "objects": {
+                "bbox": [[] for _ in range(num_images)],
+                "category": [[] for _ in range(num_images)],
+                "score": [[] for _ in range(num_images)],
+            }
+        }
+
+    # Convert to HuggingFace object detection format (dict-of-lists)
+    batch_bboxes = []
+    batch_categories = []
+    batch_scores = []
 
-    # Convert to HuggingFace object detection format
-    batch_objects = []
     for result in results:
         boxes = result.get("boxes", torch.tensor([]))
         scores = result.get("scores", torch.tensor([]))
 
         # Handle empty results
         if len(boxes) == 0:
-            batch_objects.append([])
+            batch_bboxes.append([])
+            batch_categories.append([])
+            batch_scores.append([])
             continue
 
-        # Build list of detections
-        detections = []
+        # Build lists for this image
+        image_bboxes = []
+        image_categories = []
+        image_scores = []
+
         for box, score in zip(boxes.cpu().numpy(), scores.cpu().numpy()):
             x1, y1, x2, y2 = box
             width = x2 - x1
             height = y2 - y1
 
-            detection = {
-                "bbox": [float(x1), float(y1), float(width), float(height)],
-                "category": 0,  # Single class, always index 0
-                "score": float(score),
-            }
-            detections.append(detection)
+            image_bboxes.append([float(x1), float(y1), float(width), float(height)])
+            image_categories.append(0)  # Single class, always index 0
+            image_scores.append(float(score))
 
-        batch_objects.append(detections)
+        batch_bboxes.append(image_bboxes)
+        batch_categories.append(image_categories)
+        batch_scores.append(image_scores)
 
-    return {"objects": batch_objects}
+    return {
+        "objects": {
+            "bbox": batch_bboxes,
+            "category": batch_categories,
+            "score": batch_scores,
+        }
+    }
 
 
 def main():
@@ -328,16 +348,14 @@ def main():
         logger.error("Ensure the model exists and you have access permissions")
         sys.exit(1)
 
-    # Define output schema before processing
+    # Define output schema before processing (dict-of-lists format for object detection)
     logger.info("📊 Creating output schema...")
     new_features = dataset.features.copy()
-    new_features["objects"] = [
-        {
-            "bbox": [float, float, float, float],
-            "category": class_name,
-            "score": float,
-        }
-    ]
+    new_features["objects"] = {
+        "bbox": Sequence(Sequence(Value("float32"), length=4)),
+        "category": Sequence(ClassLabel(names=[class_name])),
+        "score": Sequence(Value("float32")),
+    }
 
     # Process dataset with explicit output features
     logger.info("🔍 Processing images...")