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102 lines
7.3 KiB
102 lines
7.3 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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| `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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