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

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Glenn Jocher
2023-06-25 23:59:28 +02:00
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6
docs/datasets/detect/index.md
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@ -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)

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docs/datasets/pose/index.md
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@ -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 ""

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docs/datasets/segment/index.md
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@ -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

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docs/models/fast-sam.md
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@ -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

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ultralytics/__init__.py
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@ -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