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>

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) \
-                    if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist()))  # label format
+                line = (cls, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
                 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 = seg.reshape(-1)  # (n,2) to (n*2)
-                line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg)  # label format
+                seg = mask.segments[len(det) - j - 1].copy().reshape(-1)  # reversed mask.segments, (n,2) to (n*2)
+                line = (cls, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
                 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: