train_detectron2_deeplabv3plus

Train DeepLabV3+ model for semantic segmentation. Implementation from Detectron2 (Meta Research).

🚀 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 data 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 train algorithm 
train = wf.add_task(name="train_detectron2_deeplabv3plus", 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

• epochs (int) - default '1000': Number of complete passes through the training dataset.
• max_iter (int) - default '1000': Maximum number of iterations.
• classes (int) - default '2': Number of classes
• input_width (int) - default '800': Size width of the input image.
• input_height (int) - default '800': Size height of the input image.
• batch_size (int) - default '4': Number of samples processed before the model is updated.
• learning_rate (float) - default '0.02': Step size at which the model's parameters are updated during training.
• eval_period (int) - default '100: Interval between evaluations.
• 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 the training config file .yaml.
• warmupFactor (float) - default '0.001':
• warmupIters (int) - default '200':
• polyLRFactor (float) - default '0.9':
• polyLRConstantFactor (float) - default '0.0':
• resnetDepth (int) - default '50':
• batchNorm (str) - default 'BN':
• early_stopping (bool) - default 'False':
• patience (int) - default '10':
• numGPU (int) - default '1':

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()    

# Add data 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 train algorithm 
train = wf.add_task(name="train_detectron2_deeplabv3plus", auto_connect=True)
train.set_parameters({
    "batch_size": "4",
    "epochs": "50",
    "learning_rate": "0.02",
    "dataset_split_ratio": "80",
    "max_iter": "1000",
    "classes": "2",
    "warmupFactor": "0.001",
    "warmupIters": "200",
    "polyLRFactor": "0.9",
    "batchNorm": "BN",
    "early_stopping": "False"
}) 

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