Convert YOLOv5 to Supervisely format

Overview • Preparation • How To Run • How To Use

Overview

🗄️ Starting from version 1.2.2 the application supports the import of multiple projects at once. Each project should be placed in a separate directory with the correct structure (see below).

The app transforms folder or tar archive with images and labels in YOLOv5 format to Supervisely format and uploads data to Supervisely Platform.

Preparation

Upload images and labels in YOLO v5 format to Team Files. It is possible to upload folders or .tar archives:

• coco128 example download ➡️ extract ➡️ convert folder
• lemons.tar example download ➡️ convert archive

Example of data_config.yaml:

names: [kiwi, lemon] # class names
colors: [[255, 1, 1], [1, 255, 1]] # class colors
nc: 2 # number of classes
train: ../lemons/images/train # path to train imgs (or "images/train")
val: ../lemons/images/val # path to val imgs (or "images/val")

Project Tree example for Folder and Archive

Note: YOLO v5 project must contain data_config.yaml file in its root directory if you want to use custom classes, or it will use default coco class names:

# class names
names:
  [
    "person",
    "bicycle",
    "car",
    "motorcycle",
    "airplane",
    "bus",
    "train",
    "truck",
    "boat",
    "traffic light",
    "fire hydrant",
    "stop sign",
    "parking meter",
    "bench",
    "bird",
    "cat",
    "dog",
    "horse",
    "sheep",
    "cow",
    "elephant",
    "bear",
    "zebra",
    "giraffe",
    "backpack",
    "umbrella",
    "handbag",
    "tie",
    "suitcase",
    "frisbee",
    "skis",
    "snowboard",
    "sports ball",
    "kite",
    "baseball bat",
    "baseball glove",
    "skateboard",
    "surfboard",
    "tennis racket",
    "bottle",
    "wine glass",
    "cup",
    "fork",
    "knife",
    "spoon",
    "bowl",
    "banana",
    "apple",
    "sandwich",
    "orange",
    "broccoli",
    "carrot",
    "hot dog",
    "pizza",
    "donut",
    "cake",
    "chair",
    "couch",
    "potted plant",
    "bed",
    "dining table",
    "toilet",
    "tv",
    "laptop",
    "mouse",
    "remote",
    "keyboard",
    "cell phone",
    "microwave",
    "oven",
    "toaster",
    "sink",
    "refrigerator",
    "book",
    "clock",
    "vase",
    "scissors",
    "teddy bear",
    "hair drier",
    "toothbrush",
  ]

How To Run

Step 1: Add the app to your team from Ecosystem if it is not there. The application will be added to the Current Team->PLugins & Apps page.

Step 2: Go to Current Team->Files page, right-click on your .tar archive or YOLO v5 project and choose Run App->Convert YOLO v5 to Supervisely format. You will be redirected to the Workspace->Tasks page.

How to use

The resulting project will be saved to your current Workspace with the same name as the YOLO v5 folder or archive. The application creates 2 datasets: train and val, and additionally assigns train and val tags to the images. If there are no images in val then only the train dataset is created.

You can also access your project by clicking on its name from the Tasks page.