diff --git a/aaaaaa/ui.py b/aaaaaa/ui.py
index f637d79..697dd36 100644
--- a/aaaaaa/ui.py
+++ b/aaaaaa/ui.py
@@ -97,6 +97,11 @@ def on_ad_model_update(model: str):
             visible=True,
             placeholder="Comma separated class names to detect, ex: 'person,cat'. default: COCO 80 classes",
         )
+    if "yolo" in model.lower():
+        return gr.update(
+            visible=True,
+            placeholder="Comma separated class numbers to detect or separated class names, ex: '0,1' for first 2 classes, or 'head, hip",
+        )
     return gr.update(visible=False, placeholder="")
 
 
@@ -203,7 +208,7 @@ def one_ui_group(n: int, is_img2img: bool, webui_info: WebuiInfo):
             w.ad_model_classes = gr.Textbox(
                 label="ADetailer detector classes" + suffix(n),
                 value="",
-                visible=False,
+                visible=True,
                 elem_id=eid("ad_model_classes"),
             )
 
diff --git a/adetailer/ultralytics.py b/adetailer/ultralytics.py
index 7c7a1a7..a70185d 100644
--- a/adetailer/ultralytics.py
+++ b/adetailer/ultralytics.py
@@ -4,6 +4,7 @@
 from typing import TYPE_CHECKING
 
 import cv2
+import numpy as np
 from PIL import Image
 from torchvision.transforms.functional import to_pil_image
 
@@ -25,9 +26,30 @@ def ultralytics_predict(
     from ultralytics import YOLO
 
     model = YOLO(model_path)
-    apply_classes(model, model_path, classes)
+    class_indices = []
+    if classes:
+        parsed = [c.strip() for c in classes.split(",") if c.strip()]
+        for c in parsed:
+            if c.isdigit():
+                class_indices.append(int(c))
+            elif c in model.names.values():
+                # Find the index for the class name
+                for idx, name in model.names.items():
+                    if name == c:
+                        class_indices.append(idx)
+                        break
+
     pred = model(image, conf=confidence, device=device)
 
+    if class_indices and len(pred[0].boxes) > 0:
+        cls = pred[0].boxes.cls.cpu().numpy()
+        mask = np.isin(cls, class_indices)
+
+        # Apply mask to boxes
+        pred[0].boxes.data = pred[0].boxes.data[mask]
+        if pred[0].masks is not None:
+            pred[0].masks.data = pred[0].masks.data[mask]
+
     bboxes = pred[0].boxes.xyxy.cpu().numpy()
     if bboxes.size == 0:
         return PredictOutput()
@@ -50,11 +72,27 @@ def ultralytics_predict(
 
 
 def apply_classes(model: YOLO | YOLOWorld, model_path: str | Path, classes: str):
-    if not classes or "-world" not in Path(model_path).stem:
+    if not classes:
         return
+
     parsed = [c.strip() for c in classes.split(",") if c.strip()]
-    if parsed:
-        model.set_classes(parsed)
+    if not parsed:
+        return
+
+    try:
+        class_indices = []
+        for c in parsed:
+            if c.isdigit():
+                class_indices.append(int(c))
+            elif c in model.names.values():
+                for idx, name in model.names.items():
+                    if name == c:
+                        class_indices.append(idx)
+                        break
+
+        model.classes = class_indices
+    except Exception as e:
+        print(f"Error setting classes: {e}")
 
 
 def mask_to_pil(masks: torch.Tensor, shape: tuple[int, int]) -> list[Image.Image]: