YOLOv8-16bit/docs/usage/cfg.md

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
    ```

Where:

• TASK (optional) is one of [detect, segment, classify]. 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]
• 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, and classification. These tasks differ in the type of output they produce and the specific problem they are designed to solve.

• Detect: Detection tasks involve identifying and localizing objects or regions of interest in an image or video. YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an image.
• Segment: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for each pixel in an image.
• Classify: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO models can be used for image classification tasks by predicting the class label of an input image.

Modes

YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes include train, val, and predict.

• Train: The train mode is used to train the model on a dataset. This mode is typically used during the development and testing phase of a model.
• Val: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used to tune the model's hyperparameters and detect overfitting.
• Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in production or when deploying the model to users.

Key	Value	Description
task	'detect'	inference task, i.e. detect, segment, or classify
mode	'train'	YOLO mode, i.e. train, val, predict, or export
resume	False	resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt
model	None	path to model file, i.e. yolov8n.pt, yolov8n.yaml
data	None	path to data file, i.e. coco128.yaml

Training

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	support rectangular training
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
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
fl_gamma	0.0	focal loss gamma (efficientDet default gamma=1.5)
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

Prediction

Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes to consider. Other factors that may affect the prediction process include the size and format of the input data, the presence of additional features such as masks or multiple labels per box, and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a given task.

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
hide_labels	False	hide labels
hide_conf	False	hide confidence scores
max_det	300	maximum number of detections per image
vid_stride	False	video frame-rate stride
line_thickness	3	bounding box thickness (pixels)
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]
box	True	Show boxes in segmentation predictions

Validation

Validation settings for YOLO models refer to the various hyperparameters and configurations used to evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation process include the size and composition of the validation 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 model is performing well on the validation dataset and to 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	support rectangular evaluation
split	val	dataset split to use for validation, i.e. 'val', 'test' or 'train'

Export

Export settings for YOLO models refer to the various configurations and options used to save or export the model for use in other environments or platforms. These settings can affect the model's performance, size, and compatibility with different systems. Some common YOLO export settings include the format of the exported model file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the export process include the specific task the model is being used for and the requirements or constraints of the target environment or platform. It is important to carefully consider and configure these settings to ensure that the exported model is optimized for the intended use case and can be used 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

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