train_detectron2_deeplabv3plus

train_detectron2_deeplabv3plus

About

1.2.2
Apache-2.0

Training process for DeepLabv3+ model of Detectron2.

Task: Semantic segmentation
semantic
segmentation
detectron2
facebook
atrous
convolution
encoder
decoder

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

Deeplabv3+ illustration

🚀 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()

Developer

  • Ikomia
    Ikomia

License

Apache License 2.0
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