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14 KiB
14 KiB
Argument | Default | Description |
---|---|---|
model |
None |
Specifies the model file for training. Accepts a path to either a .pt pretrained model or a .yaml configuration file. Essential for defining the model structure or initializing weights. |
data |
None |
Path to the dataset configuration file (e.g., coco8.yaml ). This file contains dataset-specific parameters, including paths to training and validation data, class names, and number of classes. |
epochs |
100 |
Total number of training epochs. Each epoch represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance. |
time |
None |
Maximum training time in hours. If set, this overrides the epochs argument, allowing training to automatically stop after the specified duration. Useful for time-constrained training scenarios. |
patience |
100 |
Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus. |
batch |
16 |
Batch size, with three modes: set as an integer (e.g., batch=16 ), auto mode for 60% GPU memory utilization (batch=-1 ), or auto mode with specified utilization fraction (batch=0.70 ). |
imgsz |
640 |
Target image size for training. All images are resized to this dimension before being fed into the model. Affects model accuracy and computational complexity. |
save |
True |
Enables saving of training checkpoints and final model weights. Useful for resuming training or model deployment. |
save_period |
-1 |
Frequency of saving model checkpoints, specified in epochs. A value of -1 disables this feature. Useful for saving interim models during long training sessions. |
cache |
False |
Enables caching of dataset images in memory (True /ram ), on disk (disk ), or disables it (False ). Improves training speed by reducing disk I/O at the cost of increased memory usage. |
device |
None |
Specifies the computational device(s) for training: a single GPU (device=0 ), multiple GPUs (device=0,1 ), CPU (device=cpu ), or MPS for Apple silicon (device=mps ). |
workers |
8 |
Number of worker threads for data loading (per RANK if Multi-GPU training). Influences the speed of data preprocessing and feeding into the model, especially useful in multi-GPU setups. |
project |
None |
Name of the project directory where training outputs are saved. Allows for organized storage of different experiments. |
name |
None |
Name of the training run. Used for creating a subdirectory within the project folder, where training logs and outputs are stored. |
exist_ok |
False |
If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs. |
pretrained |
True |
Determines whether to start training from a pretrained model. Can be a boolean value or a string path to a specific model from which to load weights. Enhances training efficiency and model performance. |
optimizer |
'auto' |
Choice of optimizer for training. Options include SGD , Adam , AdamW , NAdam , RAdam , RMSProp etc., or auto for automatic selection based on model configuration. Affects convergence speed and stability. |
verbose |
False |
Enables verbose output during training, providing detailed logs and progress updates. Useful for debugging and closely monitoring the training process. |
seed |
0 |
Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations. |
deterministic |
True |
Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms. |
single_cls |
False |
Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification. |
rect |
False |
Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy. |
cos_lr |
False |
Utilizes a cosine learning rate scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence. |
close_mosaic |
10 |
Disables mosaic data augmentation in the last N epochs to stabilize training before completion. Setting to 0 disables this feature. |
resume |
False |
Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly. |
amp |
True |
Enables Automatic Mixed Precision (AMP) training, reducing memory usage and possibly speeding up training with minimal impact on accuracy. |
fraction |
1.0 |
Specifies the fraction of the dataset to use for training. Allows for training on a subset of the full dataset, useful for experiments or when resources are limited. |
profile |
False |
Enables profiling of ONNX and TensorRT speeds during training, useful for optimizing model deployment. |
freeze |
None |
Freezes the first N layers of the model or specified layers by index, reducing the number of trainable parameters. Useful for fine-tuning or transfer learning. |
lr0 |
0.01 |
Initial learning rate (i.e. SGD=1E-2 , Adam=1E-3 ) . Adjusting this value is crucial for the optimization process, influencing how rapidly model weights are updated. |
lrf |
0.01 |
Final learning rate as a fraction of the initial rate = (lr0 * lrf ), used in conjunction with schedulers to adjust the learning rate over time. |
momentum |
0.937 |
Momentum factor for SGD or beta1 for Adam optimizers, influencing the incorporation of past gradients in the current update. |
weight_decay |
0.0005 |
L2 regularization term, penalizing large weights to prevent overfitting. |
warmup_epochs |
3.0 |
Number of epochs for learning rate warmup, gradually increasing the learning rate from a low value to the initial learning rate to stabilize training early on. |
warmup_momentum |
0.8 |
Initial momentum for warmup phase, gradually adjusting to the set momentum over the warmup period. |
warmup_bias_lr |
0.1 |
Learning rate for bias parameters during the warmup phase, helping stabilize model training in the initial epochs. |
box |
7.5 |
Weight of the box loss component in the loss function, influencing how much emphasis is placed on accurately predicting bounding box coordinates. |
cls |
0.5 |
Weight of the classification loss in the total loss function, affecting the importance of correct class prediction relative to other components. |
dfl |
1.5 |
Weight of the distribution focal loss, used in certain YOLO versions for fine-grained classification. |
pose |
12.0 |
Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints. |
kobj |
2.0 |
Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy. |
label_smoothing |
0.0 |
Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization. |
nbs |
64 |
Nominal batch size for normalization of loss. |
overlap_mask |
True |
Determines whether segmentation masks should overlap during training, applicable in instance segmentation tasks. |
mask_ratio |
4 |
Downsample ratio for segmentation masks, affecting the resolution of masks used during training. |
dropout |
0.0 |
Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training. |
val |
True |
Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset. |
plots |
False |
Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression. |