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true Master YOLOv8 settings and hyperparameters for improved model performance. Learn to use YOLO CLI commands, adjust training settings, and optimize YOLO tasks & modes. YOLOv8, settings, hyperparameters, YOLO CLI commands, YOLO tasks, YOLO modes, Ultralytics documentation, model optimization, YOLOv8 training

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.



Watch: Mastering Ultralytics YOLOv8: Configuration

Ultralytics 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:

Default ARG values are defined on this page from the cfg/defaults.yaml file.

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 }

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 }

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
time None number of hours to train for, overrides epochs if supplied
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
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 True (bool or str) whether to use a pretrained model (bool) or a model to load weights from (str)
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 10 (int) disable mosaic augmentation for final epochs (0 to disable)
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
freeze None (int or list, optional) freeze first n layers, or freeze list of layer indices during training
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
plots False save plots and images during train/val

Train Guide{ .md-button }

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.

Inference arguments:

Name Type Default Description
source str 'ultralytics/assets' source directory for images or videos
conf float 0.25 object confidence threshold for detection
iou float 0.7 intersection over union (IoU) threshold for NMS
imgsz int or tuple 640 image size as scalar or (h, w) list, i.e. (640, 480)
half bool False use half precision (FP16)
device None or str None device to run on, i.e. cuda device=0/1/2/3 or device=cpu
max_det int 300 maximum number of detections per image
vid_stride bool False video frame-rate stride
stream_buffer bool False buffer all streaming frames (True) or return the most recent frame (False)
visualize bool False visualize model features
augment bool False apply image augmentation to prediction sources
agnostic_nms bool False class-agnostic NMS
classes list[int] None filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks bool False use high-resolution segmentation masks
embed list[int] None return feature vectors/embeddings from given layers

Visualization arguments:

Name Type Default Description
show bool False show predicted images and videos if environment allows
save bool False save predicted images and videos
save_frames bool False save predicted individual video frames
save_txt bool False save results as .txt file
save_conf bool False save results with confidence scores
save_crop bool False save cropped images with results
show_labels bool True show prediction labels, i.e. 'person'
show_conf bool True show prediction confidence, i.e. '0.99'
show_boxes bool True show prediction boxes
line_width None or int None line width of the bounding boxes. Scaled to image size if None.

Predict Guide{ .md-button }

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
data None path to data file, i.e. coco128.yaml
imgsz 640 size of input images as integer
batch 16 number of images per batch (-1 for AutoBatch)
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 save plots and images during train/val
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 }

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/TensorRT: dynamic axes
simplify False ONNX/TensorRT: 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 }

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