infer_yolo_v8
About
Inference with YOLOv8 models
Run YOLOv8 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 Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolo_v8", auto_connect=True)# Run on your imagewf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")# Inpect your resultdisplay(algo.get_image_with_graphics())
☀️ Use with Ikomia Studio
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.
📝 Set algorithm parameters
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model_name (str) - default 'yolov8m': Name of the YOLOv8 pre-trained model. Other model available:
- yolov8n
- yolov8s
- yolov8l
- yolov8x
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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 '0.7': 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.
Parameters should be in strings format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolo_v8", auto_connect=True)algo.set_parameters({"model_name": "yolov8m","conf_thres": "0.5","input_size": "640","iou_thres": "0.5","cuda": "True"})# Run on your imagewf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")# Inpect your resultdisplay(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.
import ikomiafrom ikomia.dataprocess.workflow import Workflow# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolo_v8", auto_connect=True)# Run on your imagewf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")# Iterate over outputsfor output in algo.get_outputs():# Print informationprint(output)# Export it to JSONoutput.to_json()
Developer
Ikomia
License
GNU Affero General Public License v3.0
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