train_yolo_v10

Train YOLOv10 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 workflow
wf = Workflow()    

# Add dataset loader
coco = 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 algorithm
train = wf.add_task(name="train_yolo_v10", auto_connect=True)

# Launch your training on your data
wf.run()

☀️ 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 'yolov10m': Name of the YOLOv10 pre-trained model. Other model available:
- yolov10n
- yolov10s
- yolov10b
- yolov10l
- yolov10x
batch_size (int) - default '8': Number of samples processed before the model is updated.
epochs (int) - default '100': Number of complete passes through the training dataset.
dataset_split_ratio (float) – default '0.9': Divide the dataset into train and evaluation sets ]0, 1[.
input_size (int) - default '640': Size of the input image.
weight_decay (float) - default '0.0005': Amount of weight decay, regularization method.
momentum (float) - default '0.937': Optimization technique that accelerates convergence.
workers (int) - default '0': Number of worker threads for data loading (per RANK if DDP).
optimizer (str) - default '0.937': Optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
lr0 (float) - default '0.01': Initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lr1 (float) - default '0.01': Final learning rate (lr0 * lrf)
output_folder (str, optional): path to where the model will be saved.
config_file (str, optional): path to the training config file .yaml.

Parameters should be in strings format when added to the dictionary.