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ultralytics 8.0.114 automatic optimizer selection (#3037)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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Glenn Jocher
2023-06-06 01:09:27 +02:00
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parent 01273c5ca1
commit 8940a27bdb
11 changed files with 72 additions and 14 deletions
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3
docs/tasks/classify.md
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@ -6,8 +6,7 @@ description: Check YOLO class label with only one class for the whole image, usi
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
predefined classes.
<br>
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418606-adf35c62-2e11-405d-84c6-b84e7d013804.png">
The output of an image classifier is a single class label and a confidence score. Image
classification is useful when you need to know only what class an image belongs to and don't need to know where objects

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docs/tasks/detect.md
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@ -5,8 +5,7 @@ description: Learn how to use YOLOv8, an object detection model pre-trained with
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
<br>
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png">
The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.

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docs/tasks/pose.md
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@ -8,7 +8,7 @@ to as keypoints. The keypoints can represent various parts of the object such as
features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]`
coordinates.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/239691398-d62692dc-713e-4207-9908-2f6710050e5c.jpg">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418616-9811ac0b-a4a7-452a-8aba-484ba32bb4a8.png">
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually
along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific

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docs/tasks/segment.md
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@ -6,8 +6,7 @@ description: Learn what Instance segmentation is. Get pretrained YOLOv8 segment
Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
and segmenting them from the rest of the image.
<br>
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418644-7df320b8-098d-47f1-85c5-26604d761286.png">
The output of an instance segmentation model is a set of masks or
contours that outline each object in the image, along with class labels and confidence scores for each object. Instance