infer_yolo_v9

Run YOLOv9 object detection 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_yolo_v9", auto_connect=True)

# Run on your image  
wf.run_on(url="https://images.pexels.com/photos/15257870/pexels-photo-15257870.jpeg?cs=srgb&dl=pexels-vision-plug-15257870.jpg&fm=jpg&w=1280&h=853")

# Inpect 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 'yolov9-m': Name of the YOLOv9 pre-trained model. Other model available:
  • yolov9-s
  • yolov9-m
  • yolov9-c
  • yolov9-e
• conf_thres (float) default '0.25': Box threshold for the prediction [0,1].
• input_size (int) - default '640': Size of the input image.
• iou_thres (float) - default '0.5': Intersection over Union, degree of overlap between two boxes [0,1].
• cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU. Optionally, you can load a custom model:
• model_weight_file (str, optional): Path to model weights file .pt.
• class_file (str, optional): Path to classes file .yaml .

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_v9", auto_connect=True)

algo.set_parameters({
    "model_name": "yolov9-c",
    "conf_thres": "0.5",
    "input_size": "640",
    "iou_thres": "0.5",
    "cuda": "True"
})

# Run on your image  
wf.run_on(url="https://images.pexels.com/photos/15257870/pexels-photo-15257870.jpeg?cs=srgb&dl=pexels-vision-plug-15257870.jpg&fm=jpg&w=1280&h=853")

# Inpect 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_yolo_v9", auto_connect=True)

# Run on your image  
wf.run_on(url="https://images.pexels.com/photos/15257870/pexels-photo-15257870.jpeg?cs=srgb&dl=pexels-vision-plug-15257870.jpg&fm=jpg&w=1280&h=853")

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