You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 lines
6.6 KiB
88 lines
6.6 KiB
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
|
|
|
**Train mode** is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the |
|
specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can |
|
accurately predict the classes and locations of objects in an image. |
|
|
|
!!! tip "Tip" |
|
|
|
* YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml` |
|
|
|
## Usage Examples |
|
|
|
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of |
|
training arguments. |
|
|
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO("yolov8n.yaml") # build a new model from scratch |
|
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) |
|
|
|
# Train the model |
|
model.train(data="coco128.yaml", epochs=100, imgsz=640) |
|
``` |
|
=== "CLI" |
|
|
|
```bash |
|
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 |
|
``` |
|
|
|
## Arguments |
|
|
|
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 | |
|
|-------------------|----------|-----------------------------------------------------------------------------| |
|
| `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 | |
|
| `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 or w,h | |
|
| `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` | `False` | whether to use a pretrained model | |
|
| `optimizer` | `'SGD'` | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] | |
|
| `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 | |
|
| `image_weights` | `False` | use weighted image selection for training | |
|
| `rect` | `False` | support rectangular training | |
|
| `cos_lr` | `False` | use cosine learning rate scheduler | |
|
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs | |
|
| `resume` | `False` | resume training from last checkpoint | |
|
| `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 | |
|
| `fl_gamma` | `0.0` | focal loss gamma (efficientDet default gamma=1.5) | |
|
| `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 |
|
|
|