Add Docs glossary links (#16448)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -57,22 +57,22 @@ Here are all supported callbacks. See callbacks [source code](https://github.com
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### Trainer Callbacks
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| Callback | Description |
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| --------------------------- | ------------------------------------------------------- |
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| `on_pretrain_routine_start` | Triggered at the beginning of pre-training routine |
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| `on_pretrain_routine_end` | Triggered at the end of pre-training routine |
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| `on_train_start` | Triggered when the training starts |
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| `on_train_epoch_start` | Triggered at the start of each training epoch |
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| `on_train_batch_start` | Triggered at the start of each training batch |
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| `optimizer_step` | Triggered during the optimizer step |
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| `on_before_zero_grad` | Triggered before gradients are zeroed |
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| `on_train_batch_end` | Triggered at the end of each training batch |
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| `on_train_epoch_end` | Triggered at the end of each training epoch |
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| `on_fit_epoch_end` | Triggered at the end of each fit epoch |
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| `on_model_save` | Triggered when the model is saved |
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| `on_train_end` | Triggered when the training process ends |
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| `on_params_update` | Triggered when model parameters are updated |
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| `teardown` | Triggered when the training process is being cleaned up |
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| Callback | Description |
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| --------------------------- | ------------------------------------------------------------------------------------------- |
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| `on_pretrain_routine_start` | Triggered at the beginning of pre-training routine |
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| `on_pretrain_routine_end` | Triggered at the end of pre-training routine |
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| `on_train_start` | Triggered when the training starts |
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| `on_train_epoch_start` | Triggered at the start of each training [epoch](https://www.ultralytics.com/glossary/epoch) |
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| `on_train_batch_start` | Triggered at the start of each training batch |
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| `optimizer_step` | Triggered during the optimizer step |
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| `on_before_zero_grad` | Triggered before gradients are zeroed |
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| `on_train_batch_end` | Triggered at the end of each training batch |
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| `on_train_epoch_end` | Triggered at the end of each training epoch |
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| `on_fit_epoch_end` | Triggered at the end of each fit epoch |
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| `on_model_save` | Triggered when the model is saved |
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| `on_train_end` | Triggered when the training process ends |
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| `on_params_update` | Triggered when model parameters are updated |
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| `teardown` | Triggered when the training process is being cleaned up |
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### Validator Callbacks
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@ -199,7 +199,7 @@ For more comprehensive usage, refer to the [Prediction Guide](../modes/predict.m
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Ultralytics YOLO supports various practical implementations of callbacks to enhance and customize different phases like training, validation, and prediction. Some practical examples include:
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1. **Logging Custom Metrics**: Log additional metrics at different stages, such as the end of training or validation epochs.
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2. **Data Augmentation**: Implement custom data transformations or augmentations during prediction or training batches.
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2. **[Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation)**: Implement custom data transformations or augmentations during prediction or training batches.
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3. **Intermediate Results**: Save intermediate results such as predictions or frames for further analysis or visualization.
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Example: Combining frames with prediction results during prediction using `on_predict_batch_end`:
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@ -4,7 +4,7 @@ description: Optimize your YOLO model's performance with the right settings and
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keywords: YOLO, hyperparameters, configuration, training, validation, prediction, model settings, Ultralytics, performance optimization, machine learning
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---
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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings and hyperparameters can affect the model's behavior at various stages of the model development process, including training, validation, and prediction.
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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and [accuracy](https://www.ultralytics.com/glossary/accuracy). These settings and hyperparameters can affect the model's behavior at various stages of the model development process, including training, validation, and prediction.
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<p align="center">
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<br>
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@ -57,9 +57,9 @@ YOLO models can be used for a variety of tasks, including detection, segmentatio
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- **Pose**: For identifying objects and estimating their keypoints in an image or video.
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- **OBB**: Oriented (i.e. rotated) bounding boxes suitable for satellite or medical imagery.
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| Argument | Default | Description |
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| -------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `task` | `'detect'` | Specifies the YOLO task to be executed. Options include `detect` for object detection, `segment` for segmentation, `classify` for classification, `pose` for pose estimation and `OBB` for oriented bounding boxes. Each task is tailored to specific types of output and problems within image and video analysis. |
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| Argument | Default | Description |
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| -------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `task` | `'detect'` | Specifies the YOLO task to be executed. Options include `detect` for [object detection](https://www.ultralytics.com/glossary/object-detection), `segment` for segmentation, `classify` for classification, `pose` for pose estimation and `OBB` for oriented bounding boxes. Each task is tailored to specific types of output and problems within image and video analysis. |
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[Tasks Guide](../tasks/index.md){ .md-button }
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@ -82,7 +82,7 @@ YOLO models can be used in different modes depending on the specific problem you
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## Train Settings
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The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
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The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, [learning rate](https://www.ultralytics.com/glossary/learning-rate), momentum, and weight decay. Additionally, the choice of optimizer, [loss function](https://www.ultralytics.com/glossary/loss-function), and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
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{% include "macros/train-args.md" %}
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@ -90,7 +90,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
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The `batch` argument can be configured in three ways:
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- **Fixed Batch Size**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
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- **Fixed [Batch Size](https://www.ultralytics.com/glossary/batch-size)**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
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- **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
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- **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
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@ -116,7 +116,7 @@ The val (validation) settings for YOLO models involve various hyperparameters an
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{% include "macros/validation-args.md" %}
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Careful tuning and experimentation with these settings are crucial to ensure optimal performance on the validation dataset and detect and prevent overfitting.
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Careful tuning and experimentation with these settings are crucial to ensure optimal performance on the validation dataset and detect and prevent [overfitting](https://www.ultralytics.com/glossary/overfitting).
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[Val Guide](../modes/val.md){ .md-button }
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@ -132,7 +132,7 @@ It is crucial to thoughtfully configure these settings to ensure the exported mo
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## Augmentation Settings
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Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the training data, helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
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Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the [training data](https://www.ultralytics.com/glossary/training-data), helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
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{% include "macros/augmentation-args.md" %}
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@ -149,19 +149,19 @@ Logging, checkpoints, plotting, and file management are important considerations
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Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make it easier to debug and optimize the training process.
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| Argument | Default | Description |
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| ---------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `project` | `'runs'` | Specifies the root directory for saving training runs. Each run will be saved in a separate subdirectory within this directory. |
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| `name` | `'exp'` | Defines the name of the experiment. If not specified, YOLO automatically increments this name for each run, e.g., `exp`, `exp2`, etc., to avoid overwriting previous experiments. |
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| `exist_ok` | `False` | Determines whether to overwrite an existing experiment directory if one with the same name already exists. Setting this to `True` allows overwriting, while `False` prevents it. |
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| `plots` | `False` | Controls the generation and saving of training and validation plots. Set to `True` to create plots such as loss curves, precision-recall curves, and sample predictions. Useful for visually tracking model performance over time. |
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| `save` | `False` | Enables the saving of training checkpoints and final model weights. Set to `True` to periodically save model states, allowing training to be resumed from these checkpoints or models to be deployed. |
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| Argument | Default | Description |
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| ---------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `project` | `'runs'` | Specifies the root directory for saving training runs. Each run will be saved in a separate subdirectory within this directory. |
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| `name` | `'exp'` | Defines the name of the experiment. If not specified, YOLO automatically increments this name for each run, e.g., `exp`, `exp2`, etc., to avoid overwriting previous experiments. |
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| `exist_ok` | `False` | Determines whether to overwrite an existing experiment directory if one with the same name already exists. Setting this to `True` allows overwriting, while `False` prevents it. |
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| `plots` | `False` | Controls the generation and saving of training and validation plots. Set to `True` to create plots such as loss curves, [precision](https://www.ultralytics.com/glossary/precision)-[recall](https://www.ultralytics.com/glossary/recall) curves, and sample predictions. Useful for visually tracking model performance over time. |
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| `save` | `False` | Enables the saving of training checkpoints and final model weights. Set to `True` to periodically save model states, allowing training to be resumed from these checkpoints or models to be deployed. |
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## FAQ
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### How do I improve the performance of my YOLO model during training?
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Improving YOLO model performance involves tuning hyperparameters like batch size, learning rate, momentum, and weight decay. Adjusting augmentation settings, selecting the right optimizer, and employing techniques like early stopping or mixed precision can also help. For detailed guidance on training settings, refer to the [Train Guide](../modes/train.md).
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Improving YOLO model performance involves tuning hyperparameters like batch size, learning rate, momentum, and weight decay. Adjusting augmentation settings, selecting the right optimizer, and employing techniques like early stopping or [mixed precision](https://www.ultralytics.com/glossary/mixed-precision) can also help. For detailed guidance on training settings, refer to the [Train Guide](../modes/train.md).
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### What are the key hyperparameters to consider for YOLO model accuracy?
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@ -35,7 +35,7 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
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=== "Train"
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Train a detection model for 10 epochs with an initial learning_rate of 0.01
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Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
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```bash
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yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
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```
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@ -109,7 +109,7 @@ Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full
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## Val
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Validate trained YOLOv8n model accuracy on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
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Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
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!!! example
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@ -221,7 +221,7 @@ This will create `default_copy.yaml`, which you can then pass as `cfg=default_co
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### How do I use the Ultralytics YOLOv8 command line interface (CLI) for model training?
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To train a YOLOv8 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, you would run:
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To train a YOLOv8 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, you would run:
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```bash
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yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
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@ -241,7 +241,7 @@ Each task can be customized with various arguments. For detailed syntax and exam
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### How can I validate the accuracy of a trained YOLOv8 model using the CLI?
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To validate a YOLOv8 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a batch size of 1 and image size of 640, run:
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To validate a YOLOv8 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size of 640, run:
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```bash
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yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
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@ -128,7 +128,7 @@ For more details on the customization and source code, see the [`BaseTrainer` Re
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### How can I add a callback to the Ultralytics YOLOv8 DetectionTrainer?
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You can add callbacks to monitor and modify the training process in Ultralytics YOLOv8 `DetectionTrainer`. For instance, here's how you can add a callback to log model weights after every training epoch:
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You can add callbacks to monitor and modify the training process in Ultralytics YOLOv8 `DetectionTrainer`. For instance, here's how you can add a callback to log model weights after every training [epoch](https://www.ultralytics.com/glossary/epoch):
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```python
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from ultralytics.models.yolo.detect import DetectionTrainer
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@ -153,8 +153,8 @@ For further details on callback events and entry points, refer to our [Callbacks
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Ultralytics YOLOv8 offers a high-level abstraction on powerful engine executors, making it ideal for rapid development and customization. Key benefits include:
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- **Ease of Use**: Both command-line and Python interfaces simplify complex tasks.
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- **Performance**: Optimized for real-time object detection and various vision AI applications.
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- **Customization**: Easily extendable for custom models, loss functions, and dataloaders.
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- **Performance**: Optimized for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and various vision AI applications.
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- **Customization**: Easily extendable for custom models, [loss functions](https://www.ultralytics.com/glossary/loss-function), and dataloaders.
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Learn more about YOLOv8's capabilities by visiting [Ultralytics YOLO](https://www.ultralytics.com/yolo).
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@ -6,7 +6,7 @@ keywords: YOLOv8, Python, object detection, segmentation, classification, machin
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# Python Usage
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Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!
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Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into your Python projects for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!
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<p align="center">
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<br>
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@ -80,7 +80,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
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## [Val](../modes/val.md)
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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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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](https://www.ultralytics.com/glossary/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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@ -355,7 +355,7 @@ See more detailed examples in our [Predict Mode](../modes/predict.md) section.
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### What are the different modes available in YOLOv8?
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Ultralytics YOLOv8 provides various modes to cater to different machine learning workflows. These include:
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Ultralytics YOLOv8 provides various modes to cater to different [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows. These include:
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- **[Train](../modes/train.md)**: Train a model using custom datasets.
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- **[Val](../modes/val.md)**: Validate model performance on a validation set.
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@ -31,7 +31,7 @@ The `ultralytics` package comes with a myriad of utilities that can support, enh
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### Auto Labeling / Annotations
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Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO object detection model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
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Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO [object detection](https://www.ultralytics.com/glossary/object-detection) model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
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```{ .py .annotate }
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from ultralytics.data.annotator import auto_annotate
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@ -86,7 +86,7 @@ convert_coco( # (1)!
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For additional information about the `convert_coco` function, [visit the reference page](../reference/data/converter.md#ultralytics.data.converter.convert_coco)
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### Get Bounding Box Dimensions
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### Get [Bounding Box](https://www.ultralytics.com/glossary/bounding-box) Dimensions
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```{.py .annotate }
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from ultralytics.utils.plotting import Annotator
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@ -585,7 +585,7 @@ make_divisible(7, 2)
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## FAQ
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### What utilities are included in the Ultralytics package to enhance machine learning workflows?
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### What utilities are included in the Ultralytics package to enhance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows?
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The Ultralytics package includes a variety of utilities designed to streamline and optimize machine learning workflows. Key utilities include [auto-annotation](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for labeling datasets, converting COCO to YOLO format with [convert_coco](../reference/data/converter.md#ultralytics.data.converter.convert_coco), compressing images, and dataset auto-splitting. These tools aim to reduce manual effort, ensure consistency, and enhance data processing efficiency.
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