train_detectron2_instance_segmentation

train_detectron2_instance_segmentation

About

1.0.2
Apache-2.0

Train Detectron2 instance segmentation models

Task: Instance segmentation
train
detectron2
instance
segmentation

Train your custom Detectron2 instance segmentation models.

inst seg 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 dataset loader
coco = wf.add_task(name="dataset_coco")

coco.set_parameters({
"json_file": "path/to/annotation/file.json",
"image_folder": "path/to/image/folder",
"task": "instance_segmentation",
})

# Add training algorithm
train = wf.add_task(name="train_detectron2_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

  • model_name (str) - default 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x': Name of the pre-trained model. Additional model available:

    • COCO-InstanceSegmentation\mask_rcnn_R_101_C4_3x
    • COCO-InstanceSegmentation\mask_rcnn_R_101_DC5_3x
    • COCO-InstanceSegmentation\mask_rcnn_R_101_FPN_3x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_C4_1x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_C4_3x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_DC5_1x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_DC5_3x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_FPN_1x
    • COCO-InstanceSegmentation\mask_rcnn_R_50_FPN_1x_giou
    • COCO-InstanceSegmentation\mask_rcnn_R_50_FPN_3x
    • COCO-InstanceSegmentation\mask_rcnn_X_101_32x8d_FPN_3x
    • LVISv0.5-InstanceSegmentation\mask_rcnn_R_101_FPN_1x
    • LVISv0.5-InstanceSegmentation\mask_rcnn_R_50_FPN_1x
    • LVISv0.5-InstanceSegmentation\mask_rcnn_X_101_32x8d_FPN_1x
    • LVISv1-InstanceSegmentation\mask_rcnn_R_101_FPN_1x
    • LVISv1-InstanceSegmentation\mask_rcnn_R_50_FPN_1x
    • LVISv1-InstanceSegmentation\mask_rcnn_X_101_32x8d_FPN_1x
  • max_iter (int) - default '100': Maximum number of iterations.

  • batch_size (int) - default '2': Number of samples processed before the model is updated.

  • input_size (int) - default '400': Size of the input image.

  • output_folder (str, optional): path to where the model will be saved.

  • learning_rate (float) - default '0.0025': Step size at which the model's parameters are updated during training.

  • eval_period (int) - default '50': Interval between evalutions.

  • dataset_split_ratio (float) – default '0.8' ]0, 1[: Divide the dataset into train and evaluation sets.

  • config_file(str, optional): Path to config file.

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/annotation/file.json",
"image_folder": "path/to/image/folder",
"task": "instance_segmentation",
})

# Add training algorithm
train = wf.add_task(name="train_detectron2_instance_segmentation", auto_connect=True)
train.set_parameters({
"model_name": "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x",
"batch_size": "2",
"input_size": "400",
"learning_rate": "0.0025",
"dataset_split_ratio": "0.8",
})

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

Developer

  • Ikomia
    Ikomia

License

Apache License 2.0
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