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162 lines
10 KiB
162 lines
10 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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keywords: YOLOv8, train mode, train a custom YOLOv8 model, hyperparameters, train a model, Comet, ClearML, TensorBoard, logging, loggers |
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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` | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] | |
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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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| `profile` | `False` | profile ONNX and TensorRT speeds during training for loggers | |
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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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## Logging |
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In training a YOLOv8 model, you might find it valuable to keep track of the model's performance over time. This is where logging comes into play. Ultralytics' YOLO provides support for three types of loggers - Comet, ClearML, and TensorBoard. |
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To use a logger, select it from the dropdown menu in the code snippet above and run it. The chosen logger will be installed and initialized. |
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### Comet |
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[Comet](https://www.comet.ml/site/) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking. |
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To use Comet: |
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```python |
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# pip install comet_ml |
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import comet_ml |
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comet_ml.init() |
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``` |
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Remember to sign in to your Comet account on their website and get your API key. You will need to add this to your environment variables or your script to log your experiments. |
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### ClearML |
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[ClearML](https://www.clear.ml/) is an open-source platform that automates tracking of experiments and helps with efficient sharing of resources. It is designed to help teams manage, execute, and reproduce their ML work more efficiently. |
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To use ClearML: |
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```python |
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# pip install clearml |
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import clearml |
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clearml.browser_login() |
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``` |
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After running this script, you will need to sign in to your ClearML account on the browser and authenticate your session. |
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### TensorBoard |
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[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for TensorFlow. It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. |
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To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb): |
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```bash |
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load_ext tensorboard |
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tensorboard --logdir ultralytics/runs # replace with 'runs' directory |
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``` |
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To use TensorBoard locally run the below command and view results at http://localhost:6006/. |
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```bash |
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tensorboard --logdir ultralytics/runs # replace with 'runs' directory |
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``` |
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This will load TensorBoard and direct it to the directory where your training logs are saved. |
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After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement. |