infer_sparseinst

Run 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
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_sparseinst", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.jpg")

# Inpect your result
display(algo.get_image_with_mask_and_graphics())

☀️ 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

• conf_thres (float) default '0.5': Confidence threshold for the prediction [0,1]

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

• model_weight_file (str, optional): Path to model weights file .pth.

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_sparseinst", auto_connect=True)

algo.set_parameters({
    "model_name": "sparse_inst_r50_giam",
    "conf_thres": "0.5",
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.jpg")

# Inpect your result
display(algo.get_image_with_mask_and_graphics())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_sparseinst", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_cat.jpg")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()