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104 lines
7.5 KiB
104 lines
7.5 KiB
---
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comments: true
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description: Learn how to train custom YOLOv8 models on various datasets, configure hyperparameters, and use Ultralytics' YOLO for seamless training.
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---
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
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**Train mode** is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the
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specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can
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accurately predict the classes and locations of objects in an image.
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!!! tip "Tip"
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* YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`
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## Usage Examples
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Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of
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training arguments.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.yaml') # build a new model from YAML
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# Train the model
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Build a new model from YAML and start training from scratch
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yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
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```
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## Arguments
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Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a
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dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings
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include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
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include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
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is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
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task.
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| Key | Value | Description |
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|-------------------|----------|-----------------------------------------------------------------------------|
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| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
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| `data` | `None` | path to data file, i.e. coco128.yaml |
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| `epochs` | `100` | number of epochs to train for |
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| `patience` | `50` | epochs to wait for no observable improvement for early stopping of training |
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| `batch` | `16` | number of images per batch (-1 for AutoBatch) |
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| `imgsz` | `640` | size of input images as integer or w,h |
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| `save` | `True` | save train checkpoints and predict results |
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| `save_period` | `-1` | Save checkpoint every x epochs (disabled if < 1) |
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| `cache` | `False` | True/ram, disk or False. Use cache for data loading |
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| `device` | `None` | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
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| `workers` | `8` | number of worker threads for data loading (per RANK if DDP) |
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| `project` | `None` | project name |
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| `name` | `None` | experiment name |
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| `exist_ok` | `False` | whether to overwrite existing experiment |
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| `pretrained` | `False` | whether to use a pretrained model |
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| `optimizer` | `'SGD'` | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] |
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| `verbose` | `False` | whether to print verbose output |
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| `seed` | `0` | random seed for reproducibility |
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| `deterministic` | `True` | whether to enable deterministic mode |
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| `single_cls` | `False` | train multi-class data as single-class |
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| `rect` | `False` | rectangular training with each batch collated for minimum padding |
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| `cos_lr` | `False` | use cosine learning rate scheduler |
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| `close_mosaic` | `0` | (int) disable mosaic augmentation for final epochs |
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| `resume` | `False` | resume training from last checkpoint |
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| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
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| `fraction` | `1.0` | dataset fraction to train on (default is 1.0, all images in train set) |
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| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
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| `lrf` | `0.01` | final learning rate (lr0 * lrf) |
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| `momentum` | `0.937` | SGD momentum/Adam beta1 |
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| `weight_decay` | `0.0005` | optimizer weight decay 5e-4 |
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| `warmup_epochs` | `3.0` | warmup epochs (fractions ok) |
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| `warmup_momentum` | `0.8` | warmup initial momentum |
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| `warmup_bias_lr` | `0.1` | warmup initial bias lr |
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| `box` | `7.5` | box loss gain |
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| `cls` | `0.5` | cls loss gain (scale with pixels) |
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| `dfl` | `1.5` | dfl loss gain |
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| `pose` | `12.0` | pose loss gain (pose-only) |
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| `kobj` | `2.0` | keypoint obj loss gain (pose-only) |
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| `label_smoothing` | `0.0` | label smoothing (fraction) |
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| `nbs` | `64` | nominal batch size |
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| `overlap_mask` | `True` | masks should overlap during training (segment train only) |
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| `mask_ratio` | `4` | mask downsample ratio (segment train only) |
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| `dropout` | `0.0` | use dropout regularization (classify train only) |
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| `val` | `True` | validate/test during training |
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