train_yolo_v9

Train on YOLOv9 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 train algorithm
train = wf.add_task(name="train_yolo_v9", 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 'yolov9-c': Model architecture to be trained. Should be one of :
  • yolov9-s
  • yolov9-m
  • yolov9-c
  • yolov9-e
• train_imgsz (int) - default '640': Size of the training image.
• test_imgsz (int) - default '640': Size of the eval image.
• epochs (int) - default '50': Number of complete passes through the training dataset.
• batch_size (int) - default '8': Number of samples processed before the model is updated.
• dataset_split_ratio (float) – default '0.9': Divide the dataset into train and evaluation sets ]0, 1[.
• output_folder (str, optional): Path to where the model will be saved.
• config_file (str, optional): Path to hyperparameters configuration file .yaml.
• dataset_folder (str, optional): Path to dataset folder.
• model_weight_file (str, optional): Path to pretrained model weights. Can be used to fine tune a model.

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 train algorithm
train = wf.add_task(name="train_yolo_v9", auto_connect=True)
train.set_parameters({
    "batch_size": "4",
    "epochs": "5",
    "train_imgsz": "640",
    "test_imgsz": "640",
    "dataset_split_ratio": "0.9"
})