train_transunet

Training process for TransUNet model. This algorithm can train TransUNet model for semantic segmentation.

🚀 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": "semantic_segmentation",
}) 

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

• input_size (int) - default '256': Size of the input image.
• epochs (int) - default '15': Number of complete passes through the training dataset.
• batch_size (int) - default '1': Number of samples processed before the model is updated.
• learning_rate (float) - default '0.01': Step size at which the model's parameters are updated during training.
• output_folder (str, optional): path to where the model will be saved.
• num_workers (int) - default '0': How many parallel subprocesses you want to activate when you are loading all your data during your training or validation.
• weight_decay (float) - default '1e-4': Amount of weight decay, regularization method.
• eval_period (int) - default '100: Interval between evaluations.
• max_iter (int) - default '1000': Maximum number of iterations.
• early_stopping (bool) - default 'False': Activate early stopping callback to avoid over fitting.
• dataset_split_ratio (int) – default '90' ]0, 100[: Divide the dataset into train and evaluation sets.
• patch_size (int) - default '16': Path size of the ViT 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": "semantic_segmentation",
}) 

# Add training algorithm
train = wf.add_task(name="train_transunet", auto_connect=True)
train.set_parameters({
    "batch_size": "1",
    "max_iter": "1000",
    "input_size": "256",
    "patch_size": "16",
    "dataset_split_ratio": "5",
    "eval_period": "50",
    "learning_rate": "0.01",
    "early_stopping": "False"
}) 

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