dataset_classification

This algorithm allows to load a classification dataset from a given folder. It can also split the dataset into train and validation folders.

Any classification training algorithms from Ikomia HUB can be connected.

🚀 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
from ikomia.utils import ik

# Init your workflow
wf = Workflow()

# Add the dataset loader to load your custom data and annotations
algo = wf.add_task(name="dataset_classification", auto_connect=False)

algo.set_parameters({"dataset_folder":"path/to/dataset/folder"})

# Add the training task to the workflow
resnet = wf.add_task(name="train_torchvision_resnet" , 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

• dataset_folder (str): Path to the dataset folder.

• split_dataset (bool, optional): If True, your dataset will be split into train and validation folders.

• dataset_split_ratio (float, optional) – default: '0.8': Divide the dataset into train and evaluation sets, ]0, 1[.

• output_folder (str, optional): Path to the output folder where the split dataset will be saved.

• seed (int, optional) - default '42': A seed value for the dataset slip.

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

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="dataset_classification", auto_connect=False)

algo.set_parameters({
    "dataset_folder":"path/to/dataset/folder",
    "split_dataset": "True",
    "dataset_split_ratio": "0.9",
    "output_folder": "path/to/output/folder",
    "seed": "25"
})

# Add the training task to the workflow
resnet = wf.add_task(name="train_torchvision_resnet" , auto_connect=True)

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

⏩ Advanced usage

The dataset_classification algorithm is designed to load datasets for training classification models from Ikomia HUB.

In addition to its primary purpose, this algorithm offers a convenient feature to effortlessly split the dataset into separate train and validation folders, adhering to the following organized structure:

Dataset_folder
├── train
│   ├── class-one
│   │   ├── IMG_1.jpg
│   │── class-two
│   │   ├── IMG_2.jpg
│   └── class-three
│       ├── IMG_3.jpg
├── val
│   ├── class-one
│   │   ├── IMG_4.jpg
│   │── class-two
│   │   ├── IMG_5.jpg
│   └── class-three
│       ├── IMG_6.jpg