infer_torchvision_mask_rcnn

Run Mask R-CNN inference model for object detection and segmentation.

🚀 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_torchvision_mask_rcnn", auto_connect=True)

# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_torchvision_mask_rcnn/main/icons/example.jpg")

# Display 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_weight_file (str, default=''): Path to model weights file .pth. If not provided, will use pretrain from torchvision
• class_file (str): Path to class file. Default to coco 2017 classes.
• conf_thres (float, default=0.5): Box threshold for the prediction [0,1]
• iou_thres (float, default=0.5): Intersection over Union, degree of overlap between two boxes. [0,1]

Note: parameter key and value should be in string format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

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

algo.set_parameters({
    "conf_thres": "0.8",
    "iou_thres": "0.8"
})

🔍 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_torchvision_mask_rcnn", auto_connect=True)

# Run on your image  
wf.run_on(url="example_image.png")

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