Add detect.py --hide-conf --hide-labels --line-thickness options
diff --git a/detect.py b/detect.py
index d90d2aa8c4f..358ef9e3eb1 100644
--- a/detect.py
+++ b/detect.py
@@ -110,8 +110,9 @@ def detect(opt):
 
                     if save_img or opt.save_crop or view_img:  # Add bbox to image
                         c = int(cls)  # integer class
-                        label = f'{names[c]} {conf:.2f}'
-                        plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=3)
+                        label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
+
+                        plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=opt.line_thickness)
                         if opt.save_crop:
                             save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
@@ -169,6 +170,9 @@ def detect(opt):
     parser.add_argument('--project', default='runs/detect', help='save results to project/name')
     parser.add_argument('--name', default='exp', help='save results to project/name')
     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=True, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=True, action='store_true', help='hide confidences')
     opt = parser.parse_args()
     print(opt)
     check_requirements(exclude=('pycocotools', 'thop'))

Three minor changes

- command line argument for specifying line thickness of bounding boxes, if not set defaults to 3 (the previous value)
- option to hide class labels (defaults to False)
- option to hide confidence values (defaults to False)

If none of the new parameters are set, nothing changes.

**Use case**
I have small images where only one class is detected. The labels and bounding boxes occlude most of the image making it hard to visually evaluate the results.
See also #2550


## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Enhanced customization for object detection visual outputs in YOLOv5.

### 📊 Key Changes
- Added flexibility to hide labels and confidences on detection outputs.
- Introduced argument to adjust bounding box line thickness.

### 🎯 Purpose & Impact
- **Purpose**: Allows users to control how detection results are visualized, making it easier to produce cleaner images for presentations or to respect privacy constraints.
- **Impact**: Users gain finer control over the visualization aspect of object detection, possibly improving the user experience for both developers presenting results and end users consuming those results. 🎨