dataset_coco

Load any dataset in COCO format to Ikomia format. Then, any training algorithms from the Ikomia marketplace can be connected to this converter.

🚀 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 algorithm
algo = wf.add_task(name="dataset_coco", auto_connect=False)

algo.set_parameters({"json_file": "path/to/annotations_file.json",
                     "image_folder": "path/to/image_folder",
                     "task": "detection"})

# Add your training algorithm. Choose it accordingly to the "task" parameter
train = wf.add_task(name="train_yolo_v8", auto_connect=True)

# Start training  
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

• json_file (str): Annotation file (.json) in COCO format. See this page for more information about the COCO format.
• image_folder (str): Folder containing images annotated in the annotation file.
• task (str) - Default "detection": Task of the dataset. It should be one of : "detection", "instance_segmentation", "semantic_segmentation" or "keypoints".
• output_folder (str) - Default "": Only needed when task=="semantic_segmentation". COCO format does not support semantic segmentation so we need to compute semantic segmentation masks from instance segmentation masks, and store the computed masks in a folder determined by this parameter.

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

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="dataset_coco", auto_connect=True)

algo.set_parameters({"json_file": "path/to/annotations_file.json",
                     "image_folder": "path/to/image_folder",
                     "task": "detection",
                     "output_folder": ""})