Docs URL fixes and YAML updates (#1383)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>

.github/workflows/publish.yml

@@ -64,6 +64,6 @@ jobs:
         env:
           PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
         run: |
-          mkdocs gh-deploy || true
+          mkdocs gh-deploy --force || true
-          git checkout gh-pages
+          # git checkout gh-pages
-          git push https://$PERSONAL_ACCESS_TOKEN@github.com/ultralytics/docs gh-pages --force
+          # git push https://$PERSONAL_ACCESS_TOKEN@github.com/ultralytics/docs gh-pages --force

README.md

@@ -88,7 +88,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 #### Python
 
 YOLOv8 may also be used directly in a Python environment, and accepts the
-same [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
+same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO

README.zh-CN.md

@@ -75,7 +75,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 ```
 
 `yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)
-中可用`yolo`[参数](https://docs.ultralytics.com/cfg/)的完整列表。
+中可用`yolo`[参数](https://docs.ultralytics.com/usage/cfg/)的完整列表。
 
 ```bash
 yolo task=detect    mode=train    model=yolov8n.pt        args...
@@ -84,7 +84,7 @@ yolo task=detect    mode=train    model=yolov8n.pt        args...
                          export         yolov8n.pt        format=onnx  args...
 ```
 
-YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/cfg/)：
+YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
 
 ```python
 from ultralytics import YOLO

docs/modes/predict.md

@@ -60,17 +60,17 @@ in [yolo/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ult
 following suffixes are valid for images:
 
 | Image Suffixes | Example Predict Command          | Reference                                                                     |
-|----------------|----------------------------------|--------------------------------------------------------------------------------------|
+|----------------|----------------------------------|-------------------------------------------------------------------------------|
-| bmp            | `yolo predict source=image.bmp`  | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/gdi/bitmap-file-format)   |
+| .bmp           | `yolo predict source=image.bmp`  | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format)    |
-| dng            | `yolo predict source=image.dng`  | [Adobe](https://helpx.adobe.com/photoshop/using/digital-negative.html)               |
+| .dng           | `yolo predict source=image.dng`  | [Adobe DNG](https://www.adobe.com/products/photoshop/extend.displayTab2.html) |
-| jpeg           | `yolo predict source=image.jpeg` | [Joint Photographic Experts Group](https://jpeg.org/jpeg/)                           |
+| .jpeg          | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
-| jpg            | `yolo predict source=image.jpg`  | [Joint Photographic Experts Group](https://jpeg.org/jpeg/)                           |
+| .jpg           | `yolo predict source=image.jpg`  | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
-| mpo            | `yolo predict source=image.mpo`  | [CIPA](https://www.cipa.jp/std/documents/e/DC-007-Translation-2018-E.pdf)            |
+| .mpo           | `yolo predict source=image.mpo`  | [Multi Picture Object](https://fileinfo.com/extension/mpo)                    |
-| png            | `yolo predict source=image.png`  | [Portable Network Graphics](https://www.w3.org/TR/PNG/)                              |
+| .png           | `yolo predict source=image.png`  | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG)                |
-| tif            | `yolo predict source=image.tif`  | [Adobe](https://www.adobe.com/content/dam/acom/en/products/photoshop/pdfs/tiff6.pdf) |
+| .tif           | `yolo predict source=image.tif`  | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
-| tiff           | `yolo predict source=image.tiff` | [Adobe](https://www.adobe.com/content/dam/acom/en/products/photoshop/pdfs/tiff6.pdf) |
+| .tiff          | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
-| webp           | `yolo predict source=image.webp` | [Google Developers](https://developers.google.com/speed/webp)                        |
+| .webp          | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP)                                    |
-| pfm            | `yolo predict source=image.pfm`  | [HDR Labs](http://hdrlabs.com/tools/pfrenchy/)                                       |
+| .pfm           | `yolo predict source=image.pfm`  | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats)        |
 
 ## Video Formats
 
@@ -79,19 +79,19 @@ in [yolo/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ult
 following suffixes are valid for videos:
 
 | Video Suffixes | Example Predict Command          | Reference                                                                        |
-|----------------|----------------------------------|----------------------------------------------------------------------------------------------------------------|
+|----------------|----------------------------------|----------------------------------------------------------------------------------|
-| asf            | `yolo predict source=video.asf`  | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/wmformat/asf-file-structure)                        |
+| .asf           | `yolo predict source=video.asf`  | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) |
-| avi            | `yolo predict source=video.avi`  | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/directshow/avi-riff-file-reference)                 |
+| .avi           | `yolo predict source=video.avi`  | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave)   |
-| gif            | `yolo predict source=video.gif`  | [CompuServe](https://www.w3.org/Graphics/GIF/spec-gif89a.txt)                                                  |
+| .gif           | `yolo predict source=video.gif`  | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF)                 |
-| m4v            | `yolo predict source=video.m4v`  | [Apple](https://developer.apple.com/library/archive/documentation/QuickTime/QTFF/QTFFChap2/qtff2.html)         |
+| .m4v           | `yolo predict source=video.m4v`  | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V)                              |
-| mkv            | `yolo predict source=video.mkv`  | [Matroska](https://matroska.org/technical/specs/index.html)                                                    |
+| .mkv           | `yolo predict source=video.mkv`  | [Matroska](https://en.wikipedia.org/wiki/Matroska)                               |
-| mov            | `yolo predict source=video.mov`  | [Apple](https://developer.apple.com/library/archive/documentation/QuickTime/QTFF/QTFFPreface/qtffPreface.html) |
+| .mov           | `yolo predict source=video.mov`  | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format)     |
-| mp4            | `yolo predict source=video.mp4`  | [ISO 68939](https://www.iso.org/standard/68939.html)                                                           |
+| .mp4           | `yolo predict source=video.mp4`  | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14)       |
-| mpeg           | `yolo predict source=video.mpeg` | [ISO 56021](https://www.iso.org/standard/56021.html)                                                           |
+| .mpeg          | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
-| mpg            | `yolo predict source=video.mpg`  | [ISO 56021](https://www.iso.org/standard/56021.html)                                                           |
+| .mpg           | `yolo predict source=video.mpg`  | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
-| ts             | `yolo predict source=video.ts`   | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream)                                   |
+| .ts            | `yolo predict source=video.ts`   | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream)     |
-| wmv            | `yolo predict source=video.wmv`  | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/wmformat/wmv-file-structure)                        |
+| .wmv           | `yolo predict source=video.wmv`  | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video)         |
-| webm           | `yolo predict source=video.webm` | [Google Developers](https://developers.google.com/media/vp9/getting-started/webm-file-format)                  |
+| .webm          | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM)                               |
 
 ## Working with Results
 
@@ -163,7 +163,7 @@ Class reference documentation for `Results` module and its components can be fou
 ## Plotting results
 
 You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes,
-masks, classification logits, etc) found in the results object
+masks, classification logits, etc.) found in the results object
 
 ```python
 res = model(img)

docs/modes/track.md

@@ -52,7 +52,7 @@ do is loading the corresponding (detection or segmentation) model.
 ### Tracking
 
 Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer
-to [predict page](https://docs.ultralytics.com/cfg/#prediction).
+to [predict page](https://docs.ultralytics.com/usage/cfg/#prediction).
 !!! example ""
 
     === "Python"

docs/stylesheets/style.css

@@ -1,31 +1,14 @@
 th, td {
-    border: 1px solid var(--md-typeset-table-color);
+	border: 0.5px solid var(--md-typeset-table-color);
 	border-spacing: 0px;
 	border-bottom: none;
 	border-left: none;
 	border-top: none;
 }
-
 .md-typeset__table {
+	min-width: 100%;
 	line-height: 1;
 }
-
+.md-typeset table:not([class]) {
-.md-typeset__table table:not([class]) {
+	display: table;
-    font-size: .74rem;
-    border-right: none;
-}
-
-.md-typeset__table table:not([class]) td,
-.md-typeset__table table:not([class]) th {
-    padding: 15px;
-}
-
-/* light mode alternating table bg colors */
-.md-typeset__table tr:nth-child(2n) {
-    background-color: #f8f8f8;
-}
-
-/* dark mode alternating table bg colors */
-[data-md-color-scheme="slate"] .md-typeset__table tr:nth-child(2n) {
-    background-color: hsla(var(--md-hue),25%,25%,1)
 }

docs/tasks/classify.md

@@ -91,7 +91,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
         yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
         ```
 
-Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
+Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
 
 ## Export
 

docs/tasks/detect.md

@@ -93,7 +93,7 @@ Use a trained YOLOv8n model to run predictions on images.
         yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
         ```
 
-Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
+Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
 
 ## Export
 

docs/tasks/keypoints.md

@@ -95,7 +95,7 @@ Use a trained YOLOv8n model to run predictions on images.
         yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
         ```
 
-Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
+Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
 
 ## Export TODO
 

docs/tasks/segment.md

@@ -97,7 +97,7 @@ Use a trained YOLOv8n-seg model to run predictions on images.
         yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model
         ```
 
-Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
+Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
 
 ## Export
 

examples/tutorial.ipynb

@@ -86,7 +86,7 @@
       "source": [
         "# 1. Predict\n",
         "\n",
-        "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
+        "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
       ]
     },
     {

mkdocs.yml

@@ -51,6 +51,20 @@ extra:
   analytics:
     provider: google
     property: G-2M5EHKC0BH
+    feedback:
+      title: Was this page helpful?
+      ratings:
+        - icon: material/heart
+          name: This page was helpful
+          data: 1
+          note: Thanks for your feedback!
+        - icon: material/heart-broken
+          name: This page could be improved
+          data: 0
+          note: >-
+            Thanks for your feedback!<br>
+            <a href="https://github.com/ultralytics/ultralytics/issues/new?title=Docs+Feedback+for+{title}+page+at+{url}&labels=enhancement&template=feature-request.yml" target="_blank" rel="noopener">Tell us what we can improve.</a>
+
   social:
     - icon: fontawesome/brands/github
       link: https://github.com/ultralytics

ultralytics/datasets/Argoverse.yaml

@@ -2,7 +2,7 @@
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: yolo train data=Argoverse.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── Argoverse  ← downloads here (31.3 GB)
 

ultralytics/datasets/GlobalWheat2020.yaml

@@ -2,7 +2,7 @@
 # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: yolo train data=GlobalWheat2020.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── GlobalWheat2020  ← downloads here (7.0 GB)
 

ultralytics/datasets/ImageNet.yaml

@@ -3,7 +3,7 @@
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: yolo train task=classify data=imagenet
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── imagenet  ← downloads here (144 GB)
 

ultralytics/datasets/Objects365.yaml

@@ -2,7 +2,7 @@
 # Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: yolo train data=Objects365.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
 

ultralytics/datasets/SKU-110K.yaml

@@ -2,7 +2,7 @@
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: yolo train data=SKU-110K.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── SKU-110K  ← downloads here (13.6 GB)
 

ultralytics/datasets/VOC.yaml

@@ -2,7 +2,7 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: yolo train data=VOC.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── VOC  ← downloads here (2.8 GB)
 

ultralytics/datasets/VisDrone.yaml

@@ -2,7 +2,7 @@
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: yolo train data=VisDrone.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── VisDrone  ← downloads here (2.3 GB)
 

ultralytics/datasets/coco.yaml

@@ -2,7 +2,7 @@
 # COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: yolo train data=coco.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco  ← downloads here (20.1 GB)
 

ultralytics/datasets/coco128-seg.yaml

@@ -2,7 +2,7 @@
 # COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco128.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco128-seg  ← downloads here (7 MB)
 

ultralytics/datasets/coco128.yaml

@@ -2,7 +2,7 @@
 # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco128.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco128  ← downloads here (7 MB)
 

ultralytics/datasets/coco8-seg.yaml

@@ -2,7 +2,7 @@
 # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco8-seg.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco8-seg  ← downloads here (1 MB)
 

ultralytics/datasets/coco8.yaml

@@ -2,7 +2,7 @@
 # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco8.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco8  ← downloads here (1 MB)
 

ultralytics/datasets/xView.yaml

@@ -3,7 +3,7 @@
 # --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
 # Example usage: yolo train data=xView.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── xView  ← downloads here (20.7 GB)
 

ultralytics/models/README.md

@@ -24,7 +24,7 @@ yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
 ```
 
 They may also be used directly in a Python environment, and accepts the same
-[arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
+[arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO

ultralytics/yolo/data/scripts/get_coco.sh

@@ -3,7 +3,7 @@
 # Download COCO 2017 dataset http://cocodataset.org
 # Example usage: bash data/scripts/get_coco.sh
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco  ← downloads here
 

ultralytics/yolo/data/scripts/get_coco128.sh

@@ -3,7 +3,7 @@
 # Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
 # Example usage: bash data/scripts/get_coco128.sh
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco128  ← downloads here
 

ultralytics/yolo/data/scripts/get_imagenet.sh

@@ -3,7 +3,7 @@
 # Download ILSVRC2012 ImageNet dataset https://image-net.org
 # Example usage: bash data/scripts/get_imagenet.sh
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── imagenet  ← downloads here
 

ultralytics/yolo/engine/results.py

@@ -2,7 +2,7 @@
 """
 Ultralytics Results, Boxes and Masks classes for handling inference results
 
-Usage: See https://docs.ultralytics.com/predict/
+Usage: See https://docs.ultralytics.com/modes/predict/
 """
 
 import pprint

ultralytics/yolo/v8/detect/predict.py

@@ -63,15 +63,14 @@ class DetectionPredictor(BasePredictor):
 
         # write
         for d in reversed(det):
-            cls, conf = d.cls.squeeze(), d.conf.squeeze()
+            cls, conf, id = d.cls.squeeze(), d.conf.squeeze(), None if d.id is None else int(d.id.item())
             if self.args.save_txt:  # Write to file
-                line = (cls, *(d.xywhn.view(-1).tolist()), conf) \
+                line = (cls, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
-                    if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist()))  # label format
                 with open(f'{self.txt_path}.txt', 'a') as f:
                     f.write(('%g ' * len(line)).rstrip() % line + '\n')
             if self.args.save or self.args.save_crop or self.args.show:  # Add bbox to image
                 c = int(cls)  # integer class
-                name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]
+                name = ('' if id is None else f'id:{id} ') + self.model.names[c]
                 label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')
                 self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
             if self.args.save_crop:

ultralytics/yolo/v8/segment/predict.py

@@ -76,17 +76,15 @@ class SegmentationPredictor(DetectionPredictor):
 
         # Write results
         for j, d in enumerate(reversed(det)):
-            cls, conf = d.cls.squeeze(), d.conf.squeeze()
+            cls, conf, id = d.cls.squeeze(), d.conf.squeeze(), None if d.id is None else int(d.id.item())
             if self.args.save_txt:  # Write to file
-                seg = mask.segments[len(det) - j - 1].copy()  # reversed mask.segments
+                seg = mask.segments[len(det) - j - 1].copy().reshape(-1)  # reversed mask.segments, (n,2) to (n*2)
-                seg = seg.reshape(-1)  # (n,2) to (n*2)
+                line = (cls, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
-                line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg)  # label format
                 with open(f'{self.txt_path}.txt', 'a') as f:
                     f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
             if self.args.save or self.args.save_crop or self.args.show:  # Add bbox to image
                 c = int(cls)  # integer class
-                name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]
+                name = ('' if id is None else f'id:{id} ') + self.model.names[c]
                 label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')
                 self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.boxes else None
             if self.args.save_crop: