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Configuration

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:

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 null path to model file, i.e. yolov8n.pt, yolov8n.yaml
data null path to data file, i.e. 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 null path to model file, i.e. yolov8n.pt, yolov8n.yaml
data null path to data file, i.e. 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
cache False True/ram, disk or False. Use cache for data loading
device null 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 null project name
name null 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)

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
show False show results if possible
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
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 null filter results by class, i.e. class=0, or class=[0,2,3]

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
val True validate/test during training
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 (default 0.25 predict, 0.001 val)
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)
dnn False use OpenCV DNN for ONNX inference
plots False show plots during training

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.

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