| Argument          | Type                     | Default  | Description                                                                                                                                                                                                                                                  |
| ----------------- | ------------------------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model`           | `str`                    | `None`   | Specifies the model file for training. Accepts a path to either a `.pt` pretrained model or a `.yaml` configuration file. Essential for defining the model structure or initializing weights.                                                                |
| `data`            | `str`                    | `None`   | Path to the dataset configuration file (e.g., `coco8.yaml`). This file contains dataset-specific parameters, including paths to training and [validation data](https://www.ultralytics.com/glossary/validation-data), class names, and number of classes.    |
| `epochs`          | `int`                    | `100`    | Total number of training epochs. Each [epoch](https://www.ultralytics.com/glossary/epoch) represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance.                                           |
| `time`            | `float`                  | `None`   | Maximum training time in hours. If set, this overrides the `epochs` argument, allowing training to automatically stop after the specified duration. Useful for time-constrained training scenarios.                                                          |
| `patience`        | `int`                    | `100`    | Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent [overfitting](https://www.ultralytics.com/glossary/overfitting) by stopping training when performance plateaus.                         |
| `batch`           | `int`                    | `16`     | [Batch size](https://www.ultralytics.com/glossary/batch-size), with three modes: set as an integer (e.g., `batch=16`), auto mode for 60% GPU memory utilization (`batch=-1`), or auto mode with specified utilization fraction (`batch=0.70`).               |
| `imgsz`           | `int` or `list`          | `640`    | Target image size for training. All images are resized to this dimension before being fed into the model. Affects model [accuracy](https://www.ultralytics.com/glossary/accuracy) and computational complexity.                                              |
| `save`            | `bool`                   | `True`   | Enables saving of training checkpoints and final model weights. Useful for resuming training or [model deployment](https://www.ultralytics.com/glossary/model-deployment).                                                                                   |
| `save_period`     | `int`                    | `-1`     | Frequency of saving model checkpoints, specified in epochs. A value of -1 disables this feature. Useful for saving interim models during long training sessions.                                                                                             |
| `cache`           | `bool`                   | `False`  | Enables caching of dataset images in memory (`True`/`ram`), on disk (`disk`), or disables it (`False`). Improves training speed by reducing disk I/O at the cost of increased memory usage.                                                                  |
| `device`          | `int` or `str` or `list` | `None`   | Specifies the computational device(s) for training: a single GPU (`device=0`), multiple GPUs (`device=0,1`), CPU (`device=cpu`), or MPS for Apple silicon (`device=mps`).                                                                                    |
| `workers`         | `int`                    | `8`      | Number of worker threads for data loading (per `RANK` if Multi-GPU training). Influences the speed of data preprocessing and feeding into the model, especially useful in multi-GPU setups.                                                                  |
| `project`         | `str`                    | `None`   | Name of the project directory where training outputs are saved. Allows for organized storage of different experiments.                                                                                                                                       |
| `name`            | `str`                    | `None`   | Name of the training run. Used for creating a subdirectory within the project folder, where training logs and outputs are stored.                                                                                                                            |
| `exist_ok`        | `bool`                   | `False`  | If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs.                                                                                                  |
| `pretrained`      | `bool`                   | `True`   | Determines whether to start training from a pretrained model. Can be a boolean value or a string path to a specific model from which to load weights. Enhances training efficiency and model performance.                                                    |
| `optimizer`       | `str`                    | `'auto'` | Choice of optimizer for training. Options include `SGD`, `Adam`, `AdamW`, `NAdam`, `RAdam`, `RMSProp` etc., or `auto` for automatic selection based on model configuration. Affects convergence speed and stability.                                         |
| `seed`            | `int`                    | `0`      | Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations.                                                                                                                                             |
| `deterministic`   | `bool`                   | `True`   | Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms.                                                                                                    |
| `single_cls`      | `bool`                   | `False`  | Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification.                                                                         |
| `classes`         | `list[int]`              | `None`   | Specifies a list of class IDs to train on. Useful for filtering out and focusing only on certain classes during training.                                                                                                                                    |
| `rect`            | `bool`                   | `False`  | Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy.                                                                                                              |
| `cos_lr`          | `bool`                   | `False`  | Utilizes a cosine [learning rate](https://www.ultralytics.com/glossary/learning-rate) scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence.                                   |
| `close_mosaic`    | `int`                    | `10`     | Disables mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) in the last N epochs to stabilize training before completion. Setting to 0 disables this feature.                                                                |
| `resume`          | `bool`                   | `False`  | Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly.                                                                                                        |
| `amp`             | `bool`                   | `True`   | Enables Automatic [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) (AMP) training, reducing memory usage and possibly speeding up training with minimal impact on accuracy.                                                           |
| `fraction`        | `float`                  | `1.0`    | Specifies the fraction of the dataset to use for training. Allows for training on a subset of the full dataset, useful for experiments or when resources are limited.                                                                                        |
| `profile`         | `bool`                   | `False`  | Enables profiling of ONNX and TensorRT speeds during training, useful for optimizing model deployment.                                                                                                                                                       |
| `freeze`          | `int` or `list`          | `None`   | Freezes the first N layers of the model or specified layers by index, reducing the number of trainable parameters. Useful for fine-tuning or [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).                                    |
| `lr0`             | `float`                  | `0.01`   | Initial learning rate (i.e. `SGD=1E-2`, `Adam=1E-3`) . Adjusting this value is crucial for the optimization process, influencing how rapidly model weights are updated.                                                                                      |
| `lrf`             | `float`                  | `0.01`   | Final learning rate as a fraction of the initial rate = (`lr0 * lrf`), used in conjunction with schedulers to adjust the learning rate over time.                                                                                                            |
| `momentum`        | `float`                  | `0.937`  | Momentum factor for SGD or beta1 for [Adam optimizers](https://www.ultralytics.com/glossary/adam-optimizer), influencing the incorporation of past gradients in the current update.                                                                          |
| `weight_decay`    | `float`                  | `0.0005` | L2 [regularization](https://www.ultralytics.com/glossary/regularization) term, penalizing large weights to prevent overfitting.                                                                                                                              |
| `warmup_epochs`   | `float`                  | `3.0`    | Number of epochs for learning rate warmup, gradually increasing the learning rate from a low value to the initial learning rate to stabilize training early on.                                                                                              |
| `warmup_momentum` | `float`                  | `0.8`    | Initial momentum for warmup phase, gradually adjusting to the set momentum over the warmup period.                                                                                                                                                           |
| `warmup_bias_lr`  | `float`                  | `0.1`    | Learning rate for bias parameters during the warmup phase, helping stabilize model training in the initial epochs.                                                                                                                                           |
| `box`             | `float`                  | `7.5`    | Weight of the box loss component in the [loss function](https://www.ultralytics.com/glossary/loss-function), influencing how much emphasis is placed on accurately predicting [bounding box](https://www.ultralytics.com/glossary/bounding-box) coordinates. |
| `cls`             | `float`                  | `0.5`    | Weight of the classification loss in the total loss function, affecting the importance of correct class prediction relative to other components.                                                                                                             |
| `dfl`             | `float`                  | `1.5`    | Weight of the distribution focal loss, used in certain YOLO versions for fine-grained classification.                                                                                                                                                        |
| `pose`            | `float`                  | `12.0`   | Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints.                                                                                                                             |
| `kobj`            | `float`                  | `2.0`    | Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy.                                                                                                                                         |
| `nbs`             | `int`                    | `64`     | Nominal batch size for normalization of loss.                                                                                                                                                                                                                |
| `overlap_mask`    | `bool`                   | `True`   | Determines whether object masks should be merged into a single mask for training, or kept separate for each object. In case of overlap, the smaller mask is overlayed on top of the larger mask during merge.                                                |
| `mask_ratio`      | `int`                    | `4`      | Downsample ratio for segmentation masks, affecting the resolution of masks used during training.                                                                                                                                                             |
| `dropout`         | `float`                  | `0.0`    | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training.                                                                                                                                  |
| `val`             | `bool`                   | `True`   | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.                                                                                                                                             |
| `plots`           | `bool`                   | `False`  | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.                                                                                     |
