train_sparseinst

Train Sparseinst instance segmentation models.

🚀 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_sparseinst", 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 'sparse_inst_r50_giam_aug': Name of the Sparseinst model. Additional models are available:

  • sparse_inst_r50vd_base
  • sparse_inst_r50_giam
  • sparse_inst_r50_giam_soft
  • sparse_inst_r50_giam_aug
  • sparse_inst_r50_dcn_giam_aug
  • sparse_inst_r50vd_giam_aug
  • sparse_inst_r50vd_dcn_giam_aug
  • sparse_inst_r101_giam
  • sparse_inst_r101_dcn_giam
  • sparse_inst_pvt_b1_giam
  • sparse_inst_pvt_b2_li_giam

• batch_size (int) - default '8': Number of samples processed before the model is updated.

• max_iter (int) - default '4000': Maximum number of iterations.

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

• dataset_split_ratio (float) – default '0.9': Divide the dataset into train and evaluation sets ]0, 1[.

• 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 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_sparseinst", auto_connect=True)
train.set_parameters({
    "model_name": "sparse_inst_r50vd_base",
    "batch_size": "4",
    "max_iter": "1000",
    "eval_period": "100",
    "dataset_split_ratio": "0.8",
}) 

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