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Yolo

Overview

This converter allows to import images with annotations in YOLO format for segmentation, detection and pose estimation tasks.

Each image should have a corresponding .txt file with the same name, which contains information about objects in the image.

  • Segmentation labels will be converted to polygons. Labels format: <class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>

  • Detection labels will be converted to rectangles. Labels format: <class-index> <x_center> <y_center> <width> <height>

  • Pose estimation labels will be converted to keypoints. Labels format: <class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn> for Dim=2 and <class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility> for Dim=3.

YOLO format data should have a specific configuration file that contains information about classes and datasets, usually named data_config.yaml.

⚠️ Note: If the input data does not contain data_config.yaml file, it will use default COCO class names.

Result of the import

Enterprise users have access to "Import as links" option, which supports import of this format with annotations. This option might be beneficial in many cases, as it allows data import to Supervisely platform without re-uploading, maintaining a single source and speeding up import process.

To step up import speed even further you can compress all annotation files (.txt's) into an archive and import it together with the images. (Note: This method is format-dependent and may not apply to all formats.)

Default COCO 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",
  ]

Format description

Supported image formats: .jpg, .jpeg, .mpo, .bmp, .png, .webp, .tiff, .tif, .jfif, .avif, .heic, and .heif With annotations: Yes Supported annotation file extension: .txt. Grouped by: Any structure (will be uploaded as a single dataset)\

Input files structure

Example data: download ⬇️\

Recommended directory structure:

  📂project name
   ┣ 📂images
   ┃  ┣ 📂train
   ┃  ┃  ┣ 🖼️IMG_0748.jpeg
   ┃  ┃  ┣ 🖼️IMG_1836.jpeg
   ┃  ┃  ┣ 🖼️IMG_2084.jpeg
   ┃  ┃  ┗ 🖼️IMG_3861.jpeg
   ┃  ┗ 📂val
   ┃     ┣ 🖼️IMG_4451.jpeg
   ┃     ┗ 🖼️IMG_8144.jpeg
   ┣ 📂labels
   ┃  ┣ 📂train
   ┃  ┃  ┣ 📜IMG_0748.txt
   ┃  ┃  ┣ 📜IMG_1836.txt
   ┃  ┃  ┣ 📜IMG_2084.txt
   ┃  ┃  ┗ 📜IMG_3861.txt
   ┃  ┗ 📂val
   ┃     ┣ 📜IMG_4451.txt
   ┃     ┗ 📜IMG_8144.txt
   ┗ 📜data_config.yaml

Format Config File

File data_config.yaml should contain the following keys:

  • names - a list of class names

  • colors - a list of class colors in RGB format

  • nc - the number of classes

  • train - the path to the train images

  • val - the path to the validation images

📜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")

# Keypoints (for pose estimation)
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)

Single-Image Annotation

Annotation files are in .txt format and should contain object labels on each line:

  • Class numbers that correspond to the class names in the data_config.yaml file.

  • Label coordinates must be in normalized format (from 0 to 1).

1. Segmentation

Labels should be formatted with one row per object in:

<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>

2. Detection:

Labels should be formatted with one row per object in:

<class-index> <x_center> <y_center> <width> <height>

If your boxes are in pixels, you should divide x_center and width by image width, and y_center and height by image height.

3. Pose Estimation:

Labels should be formatted with one row per object.

For Dim=2:

<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>

For Dim=3:

<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> ... <pxn> <pyn> <pn-visibility>

Yolo coordinates explanation:

The label file corresponding to the below image contains 2 persons (class 0) and a tie (class 27) from original COCO classes.

📜zidan.txt:

0 0.481719 0.634028 0.690625 0.713278
0 0.741094 0.524306 0.314750 0.933389
27 0.364844 0.795833 0.078125 0.400000
Yolo coordinates explanation

Useful links

PreviousCOCONextPascal VOC

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