Update to lowercase MkDocs admonitions (#15990)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -19,7 +19,7 @@ YOLO settings and hyperparameters play a critical role in the model's performanc
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Ultralytics commands use the following syntax:
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!!! Example
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!!! example
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=== "CLI"
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@ -19,7 +19,7 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
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<strong>Watch:</strong> Mastering Ultralytics YOLOv8: CLI
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</p>
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!!! Example
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!!! example
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=== "Syntax"
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@ -79,7 +79,7 @@ Where:
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- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
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- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
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!!! Warning "Warning"
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!!! warning "Warning"
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Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
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@ -91,7 +91,7 @@ Where:
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Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](cfg.md) page.
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!!! Example "Example"
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!!! example "Example"
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=== "Train"
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@ -111,7 +111,7 @@ Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full
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Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains its training `data` and arguments as model attributes.
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!!! Example "Example"
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!!! example "Example"
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=== "Official"
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@ -131,7 +131,7 @@ Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need t
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Use a trained YOLOv8n model to run predictions on images.
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!!! Example "Example"
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!!! example "Example"
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=== "Official"
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@ -151,7 +151,7 @@ Use a trained YOLOv8n model to run predictions on images.
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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!!! Example "Example"
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!!! example "Example"
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=== "Official"
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@ -177,7 +177,7 @@ See full `export` details in the [Export](../modes/export.md) page.
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Default arguments can be overridden by simply passing them as arguments in the CLI in `arg=value` pairs.
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!!! Tip ""
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!!! tip ""
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=== "Train"
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@ -208,7 +208,7 @@ To do this first create a copy of `default.yaml` in your current working dir wit
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This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args, like `imgsz=320` in this example:
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!!! Example
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!!! example
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=== "CLI"
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@ -21,7 +21,7 @@ Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help
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For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code.
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!!! Example "Python"
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!!! example "Python"
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```python
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from ultralytics import YOLO
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@ -49,7 +49,7 @@ For example, users can load a model, train it, evaluate its performance on a val
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Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
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!!! Example "Train"
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!!! example "Train"
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=== "From pretrained(recommended)"
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@ -82,7 +82,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
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Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
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!!! Example "Val"
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!!! example "Val"
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=== "Val after training"
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@ -120,7 +120,7 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
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Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
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!!! Example "Predict"
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!!! example "Predict"
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=== "From source"
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@ -191,7 +191,7 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
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Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
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!!! Example "Export"
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!!! example "Export"
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=== "Export to ONNX"
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@ -219,7 +219,7 @@ Export mode is used for exporting a YOLOv8 model to a format that can be used fo
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Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
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!!! Example "Track"
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!!! example "Track"
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=== "Python"
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@ -242,7 +242,7 @@ Track mode is used for tracking objects in real-time using a YOLOv8 model. In th
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Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
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!!! Example "Benchmark"
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!!! example "Benchmark"
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=== "Python"
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@ -260,7 +260,7 @@ Benchmark mode is used to profile the speed and accuracy of various export forma
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Explorer API can be used to explore datasets with advanced semantic, vector-similarity and SQL search among other features. It also enabled searching for images based on their content using natural language by utilizing the power of LLMs. The Explorer API allows you to write your own dataset exploration notebooks or scripts to get insights into your datasets.
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!!! Example "Semantic Search Using Explorer"
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!!! example "Semantic Search Using Explorer"
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=== "Using Images"
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@ -304,7 +304,7 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
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`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.
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!!! Tip "Detection Trainer Example"
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!!! tip "Detection Trainer Example"
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```python
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from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
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