train_yolo_v8

Train YOLOv8 object detection models.

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()    

# Add dataset loader
coco = wf.add_task(name="dataset_coco")

coco.set_parameters({
    "json_file": "path/to/json/annotation/file",
    "image_folder": "path/to/image/folder",
    "task": "detection",
}) 

# Add training algorithm
train = wf.add_task(name="train_yolo_v8", auto_connect=True)

# Launch your training on your data
wf.run()

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

• If you haven't started using Ikomia Studio yet, download and install it from this page.

• For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

• model_name (str) - default 'yolov8m': Name of the YOLOv8 pre-trained model. Other model available:

  • yolov8n
  • yolov8s
  • yolov8l
  • yolov8x

• batch_size (int) - default '8': Number of samples processed before the model is updated.

• epochs (int) - default '100': Number of complete passes through the training dataset.

• dataset_split_ratio (float) – default '0.9': Divide the dataset into train and evaluation sets ]0, 1[.

• input_size (int) - default '640': Size of the input image.

• weight_decay (float) - default '0.0005': Amount of weight decay, regularization method.

• momentum (float) - default '0.937': Optimization technique that accelerates convergence.

• workers (int) - default '0': Number of worker threads for data loading (per RANK if DDP).

• optimizer (str) - default '0.937': Optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]

• lr0 (float) - default '0.01': Initial learning rate (i.e. SGD=1E-2, Adam=1E-3)

• lr1 (float) - default '0.01': Final learning rate (lr0 * lrf)

• output_folder (str, optional): path to where the model will be saved.

• config_file (str, optional): path to the training config file .yaml.

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()    

# Add dataset loader
coco = wf.add_task(name="dataset_coco")

coco.set_parameters({
    "json_file": "path/to/json/annotation/file",
    "image_folder": "path/to/image/folder",
    "task": "detection",
}) 

# Add training algorithm
train = wf.add_task(name="train_yolo_v8", auto_connect=True)
train.set_parameters({
    "model_name": "yolov8n",
    "epochs": "50",
    "batch_size": "4",
    "input_size": "640",
    "dataset_split_ratio": "0.9",
    "weight_decay": "0.0005",
    "momentum": "0.937",
    "workers": "0",
    "optimizer": "auto",
    "lr0": "0.01",
    "lr1": "0.01"
}) 

# Launch your training on your data
wf.run()