ultralytics/docs/modes/train.md

Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.

!!! tip "Tip"

* YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`

Usage Examples

Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of training arguments.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.yaml')  # build a new model from YAML
    model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
    model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
    
    # Train the model
    model.train(data='coco128.yaml', epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

    # Build a new model from YAML, transfer pretrained weights to it and start training
    yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
    ```

Arguments

Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a given task.

Key	Value	Description
model	None	path to model file, i.e. yolov8n.pt, yolov8n.yaml
data	None	path to data file, i.e. coco128.yaml
epochs	100	number of epochs to train for
patience	50	epochs to wait for no observable improvement for early stopping of training
batch	16	number of images per batch (-1 for AutoBatch)
imgsz	640	size of input images as integer or w,h
save	True	save train checkpoints and predict results
save_period	-1	Save checkpoint every x epochs (disabled if < 1)
cache	False	True/ram, disk or False. Use cache for data loading
device	None	device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers	8	number of worker threads for data loading (per RANK if DDP)
project	None	project name
name	None	experiment name
exist_ok	False	whether to overwrite existing experiment
pretrained	False	whether to use a pretrained model
optimizer	'SGD'	optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose	False	whether to print verbose output
seed	0	random seed for reproducibility
deterministic	True	whether to enable deterministic mode
single_cls	False	train multi-class data as single-class
image_weights	False	use weighted image selection for training
rect	False	rectangular training with each batch collated for minimum padding
cos_lr	False	use cosine learning rate scheduler
close_mosaic	10	disable mosaic augmentation for final 10 epochs
resume	False	resume training from last checkpoint
amp	True	Automatic Mixed Precision (AMP) training, choices=[True, False]
lr0	0.01	initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf	0.01	final learning rate (lr0 * lrf)
momentum	0.937	SGD momentum/Adam beta1
weight_decay	0.0005	optimizer weight decay 5e-4
warmup_epochs	3.0	warmup epochs (fractions ok)
warmup_momentum	0.8	warmup initial momentum
warmup_bias_lr	0.1	warmup initial bias lr
box	7.5	box loss gain
cls	0.5	cls loss gain (scale with pixels)
dfl	1.5	dfl loss gain
pose	12.0	pose loss gain (pose-only)
kobj	2.0	keypoint obj loss gain (pose-only)
label_smoothing	0.0	label smoothing (fraction)
nbs	64	nominal batch size
overlap_mask	True	masks should overlap during training (segment train only)
mask_ratio	4	mask downsample ratio (segment train only)
dropout	0.0	use dropout regularization (classify train only)
val	True	validate/test during training