Load any dataset in COCO format to Ikomia format. Then, any training algorithms from the Ikomia marketplace can be connected to this converter.
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 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()
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.
- 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": ""})