train_yolo_v8
About
Train YOLOv8 object detection models.
Train 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 Workflow# Init your workflowwf = Workflow()# Add dataset loadercoco = wf.add_task(name="dataset_coco")coco.set_parameters({"json_file": "path/to/json/annotation/file","image_folder": "path/to/image/folder","task": "detection",})# Add training algorithmtrain = wf.add_task(name="train_yolo_v8", auto_connect=True)# Launch your training on your datawf.run()
☀️ 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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batch_size (int) - default '8': Number of samples processed before the model is updated.
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epochs (int) - default '100': Number of complete passes through the training dataset.
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dataset_split_ratio (float) – default '0.9': Divide the dataset into train and evaluation sets ]0, 1[.
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input_size (int) - default '640': Size of the input image.
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weight_decay (float) - default '0.0005': Amount of weight decay, regularization method.
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momentum (float) - default '0.937': Optimization technique that accelerates convergence.
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workers (int) - default '0': Number of worker threads for data loading (per RANK if DDP).
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optimizer (str) - default '0.937': Optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
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lr0 (float) - default '0.01': Initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
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lr1 (float) - default '0.01': Final learning rate (lr0 * lrf)
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output_folder (str, optional): path to where the model will be saved.
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config_file (str, optional): path to the training config file .yaml.
Parameters should be in strings format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflow# Init your workflowwf = Workflow()# Add dataset loadercoco = wf.add_task(name="dataset_coco")coco.set_parameters({"json_file": "path/to/json/annotation/file","image_folder": "path/to/image/folder","task": "detection",})# Add training algorithmtrain = wf.add_task(name="train_yolo_v8", auto_connect=True)train.set_parameters({"model_name": "yolov8n","epochs": "50","batch_size": "4","input_size": "640","dataset_split_ratio": "0.9","weight_decay": "0.0005","momentum": "0.937","workers": "0","optimizer": "auto","lr0": "0.01","lr1": "0.01"})# Launch your training on your datawf.run()
Developer
Ikomia
License
GNU Affero General Public License v3.0
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Private use | Same license |
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