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
+path: ../datasets/coco8  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
+train: images/train  # train images (relative to 'path') 4 images
-val: images/train2017  # val images (relative to 'path') 128 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>
+# 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, ..]
-val: <path-to-validation-images>
+path: ../datasets/coco8-pose  # dataset root dir
-
+train: images/train  # train images (relative to 'path') 4 images
-nc: <number-of-classes>
+val: images/val  # val images (relative to 'path') 4 images
-names: [<class-1>, <class-2>, ..., <class-n>]
+test:  # test images (optional)
-
-# 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']
 
 # 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>
+# 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, ..]
-val: <path-to-validation-images>
+path: ../datasets/coco8-seg  # dataset root dir
-
+train: images/train  # train images (relative to 'path') 4 images
-nc: <number-of-classes>
+val: images/val  # val images (relative to 'path') 4 images
-names: [<class-1>, <class-2>, ..., <class-n>]
+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.
+`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.
-
-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']
-```
 
 ## Usage
 

docs/models/fast-sam.md

@@ -12,7 +12,7 @@ The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based soluti
 
 ## Overview
 
-FastSAM is designed to address the limitations of the Segment Anything Model (SAM), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses a Convolutional Neural Network (CNN)-based detector to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
+FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
 
 ## Key Features
 
@@ -22,9 +22,9 @@ FastSAM is designed to address the limitations of the Segment Anything Model (SA
 
 3. **Prompt-guided Segmentation:** FastSAM can segment any object within an image guided by various possible user interaction prompts, providing flexibility and adaptability in different scenarios.
 
-4. **Based on YOLOv8-seg:** FastSAM is based on YOLOv8-seg, an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
+4. **Based on YOLOv8-seg:** FastSAM is based on [YOLOv8-seg](../tasks/segment.md), an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
 
-5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than SAM on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
+5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than [SAM](sam.md) on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
 
 6. **Practical Applications:** The proposed approach provides a new, practical solution for a large number of vision tasks at a really high speed, tens or hundreds of times faster than current methods.
 
@@ -32,7 +32,7 @@ FastSAM is designed to address the limitations of the Segment Anything Model (SA
 
 ## Usage
 
-FastSAM is not yet available directly via the `ultralytics` package, but it is available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
+FastSAM is not yet available within the [`ultralytics` package](../quickstart.md), but it is available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
 
 ### Installation
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.122'
+__version__ = '8.0.123'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR