infer_yolo_v8_seg

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1.0.3

AGPL-3.0

Inference with YOLOv8 segmentation models

Task: Instance segmentation

YOLO

instance

segmentation

ultralytics

Run YOLOv8 instance segmentation models.

Desk object instance 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_yolo_v8_seg", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.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 'yolov8m-seg': Name of the YOLOv8 pre-trained model. Other model available:
- yolov8n-seg
- yolov8s-seg
- yolov8l-seg
- yolov8x-seg
input_size (int) - default '640': Size of the input image.
conf_thres (float) default '0.25': Box threshold for the prediction [0,1].
iou_thres (float) - default '0.7': Intersection over Union, degree of overlap between two boxes [0,1].
cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.
model_weight_file (str, optional): Path to model weights file .pt.

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_yolo_v8_seg", auto_connect=True)

algo.set_parameters({
    "model_name": "yolov8m-seg",
    "conf_thres": "0.5",
    "input_size": "640",
    "iou_thres": "0.5",
    "cuda": "True"
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.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_yolo_v8_seg", auto_connect=True)

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

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

Developer

Ikomia

License

GNU Affero General Public License v3.0

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