Run YoloV5 object detection models. Models implementation comes from the Ultralytics team based on PyTorch framework.
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_yolo_v5", 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())
Ikomia Studio offers a friendly UI with the same features as the API.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
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model_name (str) - default 'yolov5s': Name of the pre-trained model. Additional models available:
- yolov5n
- yolov5m
- yolov5l
- yolov5x
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input_size (int) - default '640': Size of the input image.
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conf_thres (float) default '0.25': Box threshold for the prediction [0,1].
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iou_thres (float) - default '0.45': Intersection over Union, degree of overlap between two boxes [0,1].
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cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.
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model_weight_file (str, optional): Path to model weights file .pt.
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_v5", auto_connect=True)
algo.set_parameters({
"model_name": "yolov5m",
"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_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_yolo_v5", 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()