train_yolo_v7_instance_segmentation

Train for YOLO v7 instance segmentation 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": "instance_segmentation",
}) 

# Add training algorithm
train = wf.add_task(name="train_yolo_v7_instance_segmentation", 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

• train_imgsz (int) - default '640': Size of the training image.
• test_imgsz (int) - default '640': Size of the eval image.
• epochs (int) - default '10': Number of complete passes through the training dataset.
• batch_size (int) - default '16': Number of samples processed before the model is updated.
• dataset_split_ratio (float) – default '90': Divide the dataset into train and evaluation sets ]0, 100[.
• output_folder (str, optional): path to where the model will be saved.
• config_file (str, optional): Path to hyperparameters configuration 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": "instance_segmentation",
}) 

# Add training algorithm
train = wf.add_task(name="train_yolo_v7_instance_segmentation", auto_connect=True)
train.set_parameters({
    "batch_size": "4",
    "epochs": "5",
    "train_imgsz": "640",
    "test_imgsz": "640",
    "dataset_split_ratio": "90"
})

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