dataset_pascal_voc

Load PascalVOC dataset. This algorithm converts a given dataset in PascalVOC 2012 format to Ikomia format.

🚀 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

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="dataset_pascal_voc")

algo.set_parameters({
    "annotation_folder": "path/to/annotation/folder",
    "dataset_folder": "path/to/image/folder",
    "class_file": "path/to/classes/file.txt",
})

train = wf.add_task(name="train_yolo_v8", auto_connect=True)

# Run on your image  
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

• annotation_folder (str): Path to the folder containing the annotation .xml files.
• dataset_folder (str): Path to the image folder.
• instance_seg_folder (str, optional): Path to segmentation masks folder‍.
• class_file (str) = Path to text file (.txt) containing class names.

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