train_torchvision_mask_rcnn

Train Mask R-CNN instance segmentation model.

🚀 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": "instance_segmentation",
}) 

# Add training algorithm
train = wf.add_task(name="train_torchvision_mask_rcnn", 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 '15': Number of complete passes through the training dataset.
• batch_size (int) - default '8': Number of samples processed before the model is updated.
• learning_rate (float) - default '0.005': Step size at which the model's parameters are updated during training.
• weight_decay (float) - default '0.0005': Amount of weight decay, regularization method.
• momentum (float) - default '0.9: Optimization technique that accelerates convergence.
• input_size (int) - default '224': Size of the input image.
• classes (int) - default '2': Number of classes
• export_pth (bool) - default 'True'
• export_onnx (bool) - default 'False'
• 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.

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": "instance_segmentation",
}) 

# Add training algorithm
train = wf.add_task(name="train_torchvision_mask_rcnn", auto_connect=True)
train.set_parameters({
    "classes": '2',
    "batch_size": "8",
    "epochs": "5",
    "input_size": "240",
    "momentum": "0.9",
    "learning_rate": "0.005",
    "weight_decay": "0.0005",
    "export_pth": "True",
    "export_onnx": "False",
}) 

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