ultralytics/docs/macros/train-args.md

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, class names, and number of classes.
epochs	int	100	Total number of training epochs. Each 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 by stopping training when performance plateaus.
batch	int or float	16	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	640	Target image size for training. Images are resized to squares with sides equal to the specified value (if rect=False), preserving aspect ratio for YOLO models but not RT-DETR. Affects model accuracy and computational complexity.
save	bool	True	Enables saving of training checkpoints and final model weights. Useful for resuming training or 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), MPS for Apple silicon (device=mps), Huawei Ascend NPU (device=npu or device=npu:0), or auto-selection of most idle GPU (device=-1) or multiple idle GPUs (device=[-1,-1])
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 or str	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, MuSGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, 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.
verbose	bool	True	Enables verbose output during training, displaying progress bars, per-epoch metrics, and additional training information in the console.
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 minimum padding strategy—images in a batch are minimally padded to reach a common size, with the longest side equal to imgsz. Can improve efficiency and speed but may affect model accuracy.
multi_scale	float	0.0	Randomly vary imgsz each batch by +/- multi_scale (e.g. 0.25 -> 0.75x to 1.25x), rounding to model stride multiples; 0.0 disables multi-scale training.
cos_lr	bool	False	Utilizes a cosine 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 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 (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.
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, influencing the incorporation of past gradients in the current update.
weight_decay	float	0.0005	L2 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, influencing how much emphasis is placed on accurately predicting 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.
cls_pw	float	0.0	Power for class weighting to handle class imbalance using inverse class frequency. 0.0 disables class weighting, 1.0 applies full inverse frequency weighting. Values between 0 and 1 provide partial weighting.
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	1.0	Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy.
rle	float	1.0	Weight of the residual log-likelihood estimation loss in pose estimation models, affecting the precision of keypoint localization.
angle	float	1.0	Weight of the angle loss in obb models, affecting the precision of oriented bounding box angle predictions.
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 overlaid 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	True	Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.
compile	bool or str	False	Enables PyTorch 2.x torch.compile graph compilation with backend='inductor'. Accepts True → "default", False → disables, or a string mode such as "default", "reduce-overhead", "max-autotune-no-cudagraphs". Falls back to eager with a warning if unsupported.
max_det	int	300	Specifies the maximum number of objects retained during validation phase of training.