yolov8/docs/usage/cfg.md

metadata

comments: true
description: >-
  Learn about YOLO settings and modes for different tasks like detection,
  segmentation etc. Train and predict with custom argparse commands.
keywords: >-
  YOLO settings, hyperparameters, YOLOv8, Ultralytics, YOLO guide, YOLO
  commands, YOLO tasks, YOLO modes, YOLO training, YOLO detect, YOLO segment,
  YOLO classify, YOLO pose, YOLO train, YOLO val, YOLO predict, YOLO export,
  YOLO track, YOLO benchmark

YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings and hyperparameters can affect the model's behavior at various stages of the model development process, including training, validation, and prediction.

YOLOv8 'yolo' CLI commands use the following syntax:

!!! example ""

=== "CLI"

    ```bash
    yolo TASK MODE ARGS
    ```

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a YOLOv8 model from a pre-trained weights file
    model = YOLO('yolov8n.pt')
     
    # Run MODE mode using the custom arguments ARGS (guess TASK)
    model.MODE(ARGS)
    ```

Where:

• TASK (optional) is one of [detect, segment, classify, pose]. If it is not passed explicitly YOLOv8 will try to guess the TASK from the model type.
• MODE (required) is one of [train, val, predict, export, track, benchmark]
• ARGS (optional) are any number of custom arg=value pairs like imgsz=320 that override defaults. For a full list of available ARGS see the Configuration page and defaults.yaml GitHub source.

Tasks

YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose. These tasks differ in the type of output they produce and the specific problem they are designed to solve.

Detect: For identifying and localizing objects or regions of interest in an image or video.
Segment: For dividing an image or video into regions or pixels that correspond to different objects or classes.
Classify: For predicting the class label of an input image.
Pose: For identifying objects and estimating their keypoints in an image or video.

Key	Value	Description
task	'detect'	YOLO task, i.e. detect, segment, classify, pose

Tasks Guide{ .md-button .md-button--primary}

Modes

YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes include:

Train: For training a YOLOv8 model on a custom dataset.
Val: For validating a YOLOv8 model after it has been trained.
Predict: For making predictions using a trained YOLOv8 model on new images or videos.
Export: For exporting a YOLOv8 model to a format that can be used for deployment.
Track: For tracking objects in real-time using a YOLOv8 model.
Benchmark: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.

Key	Value	Description
mode	'train'	YOLO mode, i.e. train, val, predict, export, track, benchmark

Modes Guide{ .md-button .md-button--primary}

Train

The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.

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	'auto'	optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
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
rect	False	rectangular training with each batch collated for minimum padding
cos_lr	False	use cosine learning rate scheduler
close_mosaic	0	(int) disable mosaic augmentation for final epochs
resume	False	resume training from last checkpoint
amp	True	Automatic Mixed Precision (AMP) training, choices=[True, False]
fraction	1.0	dataset fraction to train on (default is 1.0, all images in train set)
profile	False	profile ONNX and TensorRT speeds during training for loggers
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

Train Guide{ .md-button .md-button--primary}

Predict

The prediction settings for YOLO models encompass a range of hyperparameters and configurations that influence the model's performance, speed, and accuracy during inference on new data. Careful tuning and experimentation with these settings are essential to achieve optimal performance for a specific task. Key settings include the confidence threshold, Non-Maximum Suppression (NMS) threshold, and the number of classes considered. Additional factors affecting the prediction process are input data size and format, the presence of supplementary features such as masks or multiple labels per box, and the particular task the model is employed for.

Key	Value	Description
source	'ultralytics/assets'	source directory for images or videos
conf	0.25	object confidence threshold for detection
iou	0.7	intersection over union (IoU) threshold for NMS
half	False	use half precision (FP16)
device	None	device to run on, i.e. cuda device=0/1/2/3 or device=cpu
show	False	show results if possible
save	False	save images with results
save_txt	False	save results as .txt file
save_conf	False	save results with confidence scores
save_crop	False	save cropped images with results
show_labels	True	show object labels in plots
show_conf	True	show object confidence scores in plots
max_det	300	maximum number of detections per image
vid_stride	False	video frame-rate stride
line_width	None	The line width of the bounding boxes. If None, it is scaled to the image size.
visualize	False	visualize model features
augment	False	apply image augmentation to prediction sources
agnostic_nms	False	class-agnostic NMS
retina_masks	False	use high-resolution segmentation masks
classes	None	filter results by class, i.e. class=0, or class=[0,2,3]
boxes	True	Show boxes in segmentation predictions

Predict Guide{ .md-button .md-button--primary}

Val

The val (validation) settings for YOLO models involve various hyperparameters and configurations used to evaluate the model's performance on a validation dataset. These settings influence the model's performance, speed, and accuracy. Common YOLO validation settings include batch size, validation frequency during training, and performance evaluation metrics. Other factors affecting the validation process include the validation dataset's size and composition, as well as the specific task the model is employed for. Careful tuning and experimentation with these settings are crucial to ensure optimal performance on the validation dataset and detect and prevent overfitting.

Key	Value	Description
save_json	False	save results to JSON file
save_hybrid	False	save hybrid version of labels (labels + additional predictions)
conf	0.001	object confidence threshold for detection
iou	0.6	intersection over union (IoU) threshold for NMS
max_det	300	maximum number of detections per image
half	True	use half precision (FP16)
device	None	device to run on, i.e. cuda device=0/1/2/3 or device=cpu
dnn	False	use OpenCV DNN for ONNX inference
plots	False	show plots during training
rect	False	rectangular val with each batch collated for minimum padding
split	val	dataset split to use for validation, i.e. 'val', 'test' or 'train'

Val Guide{ .md-button .md-button--primary}

Export

Export settings for YOLO models encompass configurations and options related to saving or exporting the model for use in different environments or platforms. These settings can impact the model's performance, size, and compatibility with various systems. Key export settings include the exported model file format (e.g., ONNX, TensorFlow SavedModel), the target device (e.g., CPU, GPU), and additional features such as masks or multiple labels per box. The export process may also be affected by the model's specific task and the requirements or constraints of the destination environment or platform. It is crucial to thoughtfully configure these settings to ensure the exported model is optimized for the intended use case and functions effectively in the target environment.

Key	Value	Description
format	'torchscript'	format to export to
imgsz	640	image size as scalar or (h, w) list, i.e. (640, 480)
keras	False	use Keras for TF SavedModel export
optimize	False	TorchScript: optimize for mobile
half	False	FP16 quantization
int8	False	INT8 quantization
dynamic	False	ONNX/TF/TensorRT: dynamic axes
simplify	False	ONNX: simplify model
opset	None	ONNX: opset version (optional, defaults to latest)
workspace	4	TensorRT: workspace size (GB)
nms	False	CoreML: add NMS

Export Guide{ .md-button .md-button--primary}

Augmentation

Augmentation settings for YOLO models refer to the various transformations and modifications applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the augmentation process include the size and composition of the original dataset and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to ensure that the augmented dataset is diverse and representative enough to train a high-performing model.

Key	Value	Description
hsv_h	0.015	image HSV-Hue augmentation (fraction)
hsv_s	0.7	image HSV-Saturation augmentation (fraction)
hsv_v	0.4	image HSV-Value augmentation (fraction)
degrees	0.0	image rotation (+/- deg)
translate	0.1	image translation (+/- fraction)
scale	0.5	image scale (+/- gain)
shear	0.0	image shear (+/- deg)
perspective	0.0	image perspective (+/- fraction), range 0-0.001
flipud	0.0	image flip up-down (probability)
fliplr	0.5	image flip left-right (probability)
mosaic	1.0	image mosaic (probability)
mixup	0.0	image mixup (probability)
copy_paste	0.0	segment copy-paste (probability)

Logging, checkpoints, plotting and file management

Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.

• Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log messages to a file.
• Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows you to resume training from a previous point if the training process is interrupted or if you want to experiment with different training configurations.
• Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by generating plots using a logging library such as TensorBoard.
• File management: Managing the various files generated during the training process, such as model checkpoints, log files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of these files and make it easy to access and analyze them as needed.

Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make it easier to debug and optimize the training process.

Key	Value	Description
project	'runs'	project name
name	'exp'	experiment name. exp gets automatically incremented if not specified, i.e, exp, exp2 ...
exist_ok	False	whether to overwrite existing experiment
plots	False	save plots during train/val
save	False	save train checkpoints and predict results