Train Args - Ultralytics

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 pretrained weights. Can be a boolean value or a string path to weights to load. `pretrained=False` trains from randomly initialized weights while keeping the model architecture.
`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.