pip install ikomia
About
1.0.1
GPL-3.0
Object detection with YOLOv9 models
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.
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-syolov9-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()
Developer
Ikomia
License
GNU General Public License v3.0
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
| Permissions | Conditions | Limitations |
|---|---|---|
Commercial use | License and copyright notice | Liability |
Modification | State changes | Warranty |
Distribution | Disclose source | |
Patent use | Same license | |
Private use |
This is not legal advice: this description is for informational purposes only and does not constitute the license itself. Provided by choosealicense.com.