@@ -395,7 +395,13 @@ def video_inference_superanimal(
         )
 
         config = update_config(config, max_individuals, device)
+
         output_suffix = "_before_adapt"
+
+        if superanimal_name == "superanimal_humanbody" and video_adapt:
+            print(f"Video adaptation currently not supported for {superanimal_name}. Setting it to false.")
+            video_adapt = False
+
         if video_adapt:
             # the users can pass in many videos. For now, we only use one video for
             # video adaptation. As reported in Ye et al. 2024, one video should be
@@ -420,6 +426,7 @@ def video_inference_superanimal(
                 plot_bboxes=plot_bboxes,
                 bboxes_pcutoff=bbox_threshold,
                 create_labeled_video=create_labeled_video,
+                torchvision_detector_name=torchvision_detector_name,
             )
 
             # we prepare the pseudo dataset in the same folder of the target video
 
@@ -47,14 +47,3 @@
         videotype=".avi",
         scale_list=scale_list,
     )
-
-    print("testing superanimal_humanbody")
-    superanimal_name = "superanimal_humanbody"
-    deeplabcut.video_inference_superanimal(
-        video,
-        superanimal_name,
-        model_name="rtmpose_x",
-        detector_name="fasterrcnn_mobilenet_v3_large_fpn",
-        videotype=".avi",
-        scale_list=scale_list,
-    )
@@ -256,7 +256,6 @@ def generate_video_from_images(image_dir: Path, output_video: Path) -> None:
 
 def create_fake_project(path: Path, params: SyntheticProjectParameters) -> None:
     if path.exists():
-        print(f"[DEBUG] Path exists: {path} (is_dir={path.is_dir()}, is_file={path.is_file()})")
         raise ValueError(f"Cannot create a fake project at an existing path")
 
     scorer = "synthetic"
 
@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+"""
+Test script for superanimal_humanbody with torchvision detector
+"""
+
+from deeplabcut.pose_estimation_pytorch.apis.utils import TORCHVISION_DETECTORS, \
+    get_filtered_coco_detector_inference_runner
+from deeplabcut.pose_estimation_pytorch.models.detectors.filtered_detector import FilteredDetector
+from deeplabcut.pose_estimation_pytorch.modelzoo import load_super_animal_config
+
+def test_torchvision_detector():
+    """Test that the torchvision detector works with superanimal_humanbody"""
+    for detector_name in TORCHVISION_DETECTORS:
+
+        # Load the superanimal_humanbody config
+        superanimal_config = load_super_animal_config(
+            super_animal="superanimal_humanbody",
+            model_name="rtmpose_x",
+            detector_name=detector_name,
+        )
+        print("Config loaded successfully!")
+
+        # Test loading the torchvision detector directly
+        print("\nTesting torchvision detector loading...")
+        entry = TORCHVISION_DETECTORS[detector_name]
+        weights = entry["weights"]
+        coco_detector = entry["fn"](weights=weights, box_score_thresh=0.6)
+        coco_detector.eval()
+        print("Torchvision detector loaded successfully!")
+
+        # Test loading the FilteredDetector
+        COCO_PERSON = 1  # COCO class ID for person
+        person_detector = FilteredDetector(coco_detector, class_id=COCO_PERSON)
+        person_detector.eval()
+        print("Filtered detector loaded successfully!")
+
+        _ = get_filtered_coco_detector_inference_runner(
+            model_name=detector_name,
+            category_id=COCO_PERSON,
+            batch_size=1,
+            model_config=superanimal_config,
+        )
+        print("Filtered detector runner created successfully!")
+
+    print("\n✅ All tests passed! The torchvision detector integration is working correctly.")
+    return True
+
+if __name__ == "__main__":
+    print("Testing superanimal_humanbody with torchvision detector...")
+    success = test_torchvision_detector()
+    if success:
+        print("\n✅ Test passed! The torchvision detector works with superanimal_humanbody")
+    else:
+        print("\n❌ Test failed! There's an issue with the torchvision detector integration")