ultralytics 8.0.123 Ubuntu security and VideoWriter codec fixes (#3380)

docs/datasets/detect/index.md

@@ -16,9 +16,9 @@ The Ultralytics YOLO format is a dataset configuration format that allows you to
 
 ```yaml
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
+path: ../datasets/coco8  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
 test:  # test images (optional)
 
 # Classes (80 COCO classes)

docs/datasets/pose/index.md

@@ -38,55 +38,33 @@ Format with Dim = 3
 
 In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
 
-** Dataset file format **
+### Dataset YAML format
 
 The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
 
 ```yaml
-train: <path-to-training-images>
-val: <path-to-validation-images>
-
-nc: <number-of-classes>
-names: [<class-1>, <class-2>, ..., <class-n>]
-
-# Keypoints
-kpt_shape: [num_kpts, dim]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
-flip_idx: [n1, n2 ... , n(num_kpts)]
-```
-
-The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
-
-The `nc` field specifies the number of object classes in the dataset.
-
-The `names` field is a list of the names of the object classes. The order of the names should match the order of the object class indices in the YOLO dataset files.
-
-NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
-
-Alternatively, you can directly define class names like this:
-
-```yaml
-names:
-  0: person
-  1: bicycle
-```
-
-(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
-For example let's say there're five keypoints of facial landmark: [left eye, right eye, nose, left point of mouth, right point of mouse], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3].(just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example)
-
-** Example **
-
-```yaml
-train: data/train/
-val: data/val/
-
-nc: 2
-names: ['person', 'car']
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-pose  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
 
 # Keypoints
 kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
 flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes dictionary
+names:
+  0: person
 ```
 
+The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
+
+`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
+
+(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
+For example if we assume five keypoints of facial landmark: [left eye, right eye, nose, left mouth, right mouth], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3] (just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example).
+
 ## Usage
 
 !!! example ""

docs/datasets/segment/index.md

@@ -37,44 +37,31 @@ Here is an example of the YOLO dataset format for a single image with two object
 
 Note: The length of each row does not have to be equal.
 
-** Dataset file format **
+### Dataset YAML format
 
 The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
 
 ```yaml
-train: <path-to-training-images>
-val: <path-to-validation-images>
-
-nc: <number-of-classes>
-names: [<class-1>, <class-2>, ..., <class-n>]
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-seg  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
 
+# Classes (80 COCO classes)
+names:
+  0: person
+  1: bicycle
+  2: car
+  ...
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
 ```
 
 The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
 
-The `nc` field specifies the number of object classes in the dataset.
-
-The `names` field is a list of the names of the object classes. The order of the names should match the order of the object class indices in the YOLO dataset files.
-
-NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory.
-
-Alternatively, you can directly define class names like this:
-
-```yaml
-names:
-  0: person
-  1: bicycle
-```
-
-** Example **
-
-```yaml
-train: data/train/
-val: data/val/
-
-nc: 2
-names: ['person', 'car']
-```
+`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
 
 ## Usage