train_yolo_v11

Train YOLOv11 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_v11", 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 'yolo11m': Name of the YOLO11 pre-trained model. Other model available:
  • yolo11n
  • yolo11s
  • yolo11l
  • yolo11x
• 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). Adjusting this value is crucial for the optimization process, influencing how rapidly model weights are updated.
• lr1 (float) - default '0.01': Final learning rate as a fraction of the initial rate = (lr0 * lrf), used in conjunction with schedulers to adjust the learning rate over time.
• patience (int) - default '300': - Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus.
• output_folder (str, optional): path to where the model will be saved.
• config_file (str, optional): path to the training config file .yaml. Using a config file allows you to set all the train settings available.

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_v11", auto_connect=True)
train.set_parameters({
    "model_name": "yolo11n",
    "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",
    "patience": "0" # 0 = Patience not used
}) 

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