infer_rf_detr_seg

Run RF-DETR 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_rf_detr_seg", auto_connect=True)

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

# Inspect your result
display(algo.get_image_with_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 rf-detr-seg-medium: Name of the RF-DETR segmentation model. Available models:
  • rf-detr-seg-nano
  • rf-detr-seg-small
  • rf-detr-seg-medium
  • rf-detr-seg-base
  • rf-detr-seg-large
• input_size (int) - default 576: Size of the input image. It is adjusted automatically to a valid RF-DETR block size when needed.
• conf_thres (float) - default 0.5: Confidence threshold for predictions, between 0 and 1.
• cuda (bool): If True, run inference on GPU when CUDA is available. If False, run on CPU.
• model_weight_file (str, optional): Path to a custom RF-DETR segmentation weight file.
• config_file (str, optional): Path to a YAML file for a custom model. Required when model_weight_file is set. It must define classes, and can also define model_name.

Parameters should be in strings format when added to the dictionary.

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_rf_detr_seg", auto_connect=True)

algo.set_parameters({
    "model_name": "rf-detr-seg-medium",
    "conf_thres": "0.5",
    "input_size": "576",
    "cuda": "True"
})

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

# Inspect your result
display(algo.get_image_with_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.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

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

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

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