Inference for YOLOR object detection models.
We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.
pip install ikomia
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_yolor", 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_graphics())
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
This algorithm uses the YOLOR_p6 model.
- iou_thres (float) - default '0.45': Intersection over Union, degree of overlap between two boxes. [0,1]
- conf_thres (float) default '0.25': Box threshold for the prediction [0,1]
- input_size (int) - default '512': Size of the input image.
- config_file (str, optional): Path to the config file.
- 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_yolor", auto_connect=True)
algo.set_parameters({
"input_size": "512",
"conf_thres": "0.2",
"iou_thres": "0.45",
})
# 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_graphics())
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_yolor", 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()