yolov8_test/docs/config.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.

Properly setting and tuning these parameters can have a significant impact on the model's ability to learn effectively from the training data and generalize to new data. For example, choosing an appropriate learning rate, batch size, and optimization algorithm can greatly affect the model's convergence speed and accuracy. Similarly, setting the correct confidence threshold and non-maximum suppression (NMS) threshold can affect the model's performance on detection tasks.

It is important to carefully consider and experiment with these settings and hyperparameters to achieve the best possible performance for a given task. This can involve trial and error, as well as using techniques such as hyperparameter optimization to search for the optimal set of parameters.

In summary, YOLO settings and hyperparameters are a key factor in the success of a YOLO model, and it is important to pay careful attention to them to achieve the desired results.

Setting the operation type

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.

• Detection: 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.
• Segmentation: 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.
• Classification: 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.

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	Set the task via CLI. See Tasks for all supported tasks like - detect, segment, classify. - init is a special case that creates a copy of default.yaml configs to the current working dir
mode	train	Set the mode via CLI. It can be train, val, predict
resume	False	Resume last given task when set to True. Resume from a given checkpoint is model.pt is passed
model	null	Set the model. Format can differ for task type. Supports model_name, model.yaml & model.pt
data	null	Set the data. Format can differ for task type. Supports data.yaml, data_folder, dataset_name

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
device	''	cuda device, i.e. 0 or 0,1,2,3 or cpu. '' selects available cuda 0 device
epochs	100	Number of epochs to train
workers	8	Number of cpu workers used per process. Scales automatically with DDP
batch	16	Batch size of the dataloader
imgsz	640	Image size of data in dataloader
optimizer	SGD	Optimizer used. Supported optimizer are: Adam, SGD, RMSProp
single_cls	False	Train on multi-class data as single-class
image_weights	False	Use weighted image selection for training
rect	False	Enable rectangular training
cos_lr	False	Use cosine LR scheduler
lr0	0.01	Initial learning rate
lrf	0.01	Final OneCycleLR learning rate
momentum	0.937	Use as momentum for SGD and beta1 for Adam
weight_decay	0.0005	Optimizer weight decay
warmup_epochs	3.0	Warmup epochs. Fractions are ok.
warmup_momentum	0.8	Warmup initial momentum
warmup_bias_lr	0.1	Warmup initial bias lr
box	0.05	Box loss gain
cls	0.5	cls loss gain
cls_pw	1.0	cls BCELoss positive_weight
obj	1.0	bj loss gain (scale with pixels)
obj_pw	1.0	obj BCELoss positive_weight
iou_t	0.20	IOU training threshold
anchor_t	4.0	anchor-multiple threshold
fl_gamma	0.0	focal loss gamma
label_smoothing	0.0
nbs	64	nominal batch size
overlap_mask	True	Segmentation: Use mask overlapping during training
mask_ratio	4	Segmentation: Set mask downsampling
dropout	False	Classification: Use dropout while 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	Input source. Accepts image, folder, video, url
show	False	View the prediction images
save_txt	False	Save the results in a txt file
save_conf	False	Save the condidence scores
save_crop	Fasle
hide_labels	False	Hide the labels
hide_conf	False	Hide the confidence scores
vid_stride	False	Input video frame-rate stride
line_thickness	3	Bounding-box thickness (pixels)
visualize	False	Visualize model features
augment	False	Augmented inference
agnostic_nms	False	Class-agnostic NMS
retina_masks	False	Segmentation: High resolution masks

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
noval	False	???
save_json	False
save_hybrid	False
conf	0.001	Confidence threshold
iou	0.6	IoU threshold
max_det	300	Maximum number of detections
half	True	Use .half() mode.
dnn	False	Use OpenCV DNN for ONNX inference
plots	False

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.

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'	The project name
name:	'exp'	The run name. exp gets automatically incremented if not specified, i.e, exp, exp2 ...
exist_ok:	False	???
plots	False	Validation: Save plots while validation
nosave	False	Don't save any plots, models or files