Train timm image classification 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
# Init your workflow
wf = Workflow()
# Add dataset loader
data_loader = wf.add_task(name="dataset_classification")
data_loader.set_parameters({
"dataset_folder": "path/to/dataset/folder",
})
train = wf.add_task(name="train_timm_image_classification", auto_connect=True)
# Launch your training on your data
wf.run()
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.
- model_name (str) - default 'resnet18': Name of the pre-trained model.
- There are over 700 timm models. You can list them using: timm.list_models()
- input_size (list) - default '[224, 224]': Size of the input image.
- epochs (int) - default '100': Number of complete passes through the training dataset.
- batch_size (int) - default '16': Number of samples processed before the model is updated.
- learning_rate (float) - default '0.0050': Step size at which the model's parameters are updated during training.
- output_folder (str, optional): path to where the model will be saved.
Parameters should be in strings format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add dataset loader
data_loader = wf.add_task(name="dataset_classification")
data_loader.set_parameters({
"dataset_folder": "C:/Users/allan/OneDrive/Desktop/ik-desktop/Images/datasets/Fruit",
})
# Add train algorithm
train = wf.add_task(name="train_timm_image_classification", auto_connect=True)
train.set_parameters({
"model_name": "resnet34",
"batch_size": "8",
"epochs": "5",
"learning_rate": "0.0050",
})
# Launch your training on your data
wf.run()