| `model` | `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` | `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` | `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` | `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` | `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` | `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` | `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` | `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` | `-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` | `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` | `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` | `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` | `None` | Name of the project directory where training outputs are saved. Allows for organized storage of different experiments. |
| `name` | `None` | Name of the training run. Used for creating a subdirectory within the project folder, where training logs and outputs are stored. |
| `exist_ok` | `False` | If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs. |
| `pretrained` | `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` | `'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. |
| `verbose` | `False` | Enables verbose output during training, providing detailed logs and progress updates. Useful for debugging and closely monitoring the training process. |
| `seed` | `0` | Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations. |
| `deterministic` | `True` | Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms. |
| `single_cls` | `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. |
| `rect` | `False` | Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy. |
| `cos_lr` | `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` | `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` | `False` | Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly. |
| `amp` | `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` | `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` | `False` | Enables profiling of ONNX and TensorRT speeds during training, useful for optimizing model deployment. |
| `freeze` | `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` | `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` | `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` | `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` | `0.0005` | L2 [regularization](https://www.ultralytics.com/glossary/regularization) term, penalizing large weights to prevent overfitting. |
| `warmup_epochs` | `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` | `0.8` | Initial momentum for warmup phase, gradually adjusting to the set momentum over the warmup period. |
| `warmup_bias_lr` | `0.1` | Learning rate for bias parameters during the warmup phase, helping stabilize model training in the initial epochs. |
| `box` | `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` | `0.5` | Weight of the classification loss in the total loss function, affecting the importance of correct class prediction relative to other components. |
| `dfl` | `1.5` | Weight of the distribution focal loss, used in certain YOLO versions for fine-grained classification. |
| `pose` | `12.0` | Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints. |
| `kobj` | `2.0` | Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy. |
| `label_smoothing` | `0.0` | Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization. |
| `nbs` | `64` | Nominal batch size for normalization of loss. |
| `overlap_mask` | `True` | Determines whether segmentation masks should overlap during training, applicable in [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks. |
| `mask_ratio` | `4` | Downsample ratio for segmentation masks, affecting the resolution of masks used during training. |
| `dropout` | `0.0` | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training. |
| `val` | `True` | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset. |
| `plots` | `False` | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression. |
