train_unet
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train_unet

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1.0.0
GPL-3.0

multi-class semantic segmentation using Unet

Task: Semantic segmentation
semantic segmentation
unet
multi-class segmentation

Train UNet model for semantic segmentation.

Unet car 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_unet", 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 '128': Size of the input image.
  • epochs (int) - default '50': 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 '0.001': Step size at which the model's parameters are updated during training.
  • val_percent (int) – default '10': Divide the dataset into train and evaluation sets.
  • num_channels (int) - default '3': Number of input chanel
  • output_folder (str, optional): path to where the model will be saved.

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_unet", auto_connect=True)
train.set_parameters({
    "batch_size": "1",
    "epochs": "50",
    "input_size": "128",
    "val_percent": "10",
    "learning_rate": "0.01",
    "num_channels": "3"
}) 

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

Developer

  • Ikomia
    Ikomia

License

GNU General Public License v3.0
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