Ultralytics YOLO
Ultralytics YOLO is a format family which consists of four formats:
Dataset examples:
Ultralytics YOLO export
For export of images:
- Supported annotations
- Detection: Bounding Boxes
- Oriented bounding box: Oriented Bounding Boxes
- Segmentation: Polygons, Masks
- Pose: Skeletons
- Attributes: Not supported.
- Tracks: Not supported.
The downloaded file is a .zip archive with the following structure:
archive.zip/
├── data.yaml # configuration file
├── train.txt # list of train subset image paths
│
├── images/
│ ├── train/ # directory with images for train subset
│ │ ├── image1.jpg
│ │ ├── image2.jpg
│ │ ├── image3.jpg
│ │ └── ...
├── labels/
│ ├── train/ # directory with annotations for train subset
│ │ ├── image1.txt
│ │ ├── image2.txt
│ │ ├── image3.txt
│ │ └── ...
# train.txt:
images/<subset>/image1.jpg
images/<subset>/image2.jpg
...
# data.yaml:
path: ./ # dataset root dir
train: train.txt # train images (relative to 'path')
# Ultralytics YOLO Pose specific field
# First number is the number of points in a skeleton.
# If there are several skeletons with different number of points, it is the greatest number of points
# Second number defines the format of point info in annotation txt files
kpt_shape: [17, 3]
# Classes
names:
0: person
1: bicycle
2: car
# ...
# <image_name>.txt:
# content depends on format
# Ultralytics YOLO Detection:
# label_id - id from names field of data.yaml
# cx, cy - relative coordinates of the bbox center
# rw, rh - relative size of the bbox
# label_id cx cy rw rh
1 0.3 0.8 0.1 0.3
2 0.7 0.2 0.3 0.1
# Ultralytics YOLO Oriented Bounding Boxes:
# xn, yn - relative coordinates of the n-th point
# label_id x1 y1 x2 y2 x3 y3 x4 y4
1 0.3 0.8 0.1 0.3 0.4 0.5 0.7 0.5
2 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6
# Ultralytics YOLO Segmentation:
# xn, yn - relative coordinates of the n-th point
# label_id x1 y1 x2 y2 x3 y3 ...
1 0.3 0.8 0.1 0.3 0.4 0.5
2 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6 0.7 0.5
# Ultralytics YOLO Pose:
# cx, cy - relative coordinates of the bbox center
# rw, rh - relative size of the bbox
# xn, yn - relative coordinates of the n-th point
# vn - visibility of n-th point. 2 - visible, 1 - partially visible, 0 - not visible
# if second value in kpt_shape is 3:
# label_id cx cy rw rh x1 y1 v1 x2 y2 v2 x3 y3 v3 ...
1 0.3 0.8 0.1 0.3 0.3 0.8 2 0.1 0.3 2 0.4 0.5 2 0.0 0.0 0 0.0 0.0 0
2 0.3 0.8 0.1 0.3 0.7 0.2 2 0.3 0.1 1 0.4 0.5 0 0.5 0.6 2 0.7 0.5 2
# if second value in kpt_shape is 2:
# label_id cx cy rw rh x1 y1 x2 y2 x3 y3 ...
1 0.3 0.8 0.1 0.3 0.3 0.8 0.1 0.3 0.4 0.5 0.0 0.0 0.0 0.0
2 0.3 0.8 0.1 0.3 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6 0.7 0.5
# Note, that if there are several skeletons with different number of points,
# smaller skeletons are padded with points with coordinates 0.0 0.0 and visibility = 0
All coordinates must be normalized. It can be achieved by dividing x coordinates and widths by image width, and y coordinates and heights by image height.
Note, that in CVAT you can place an object or some parts of it outside the image, which will cause the coordinates to be outside the [0, 1] range. YOLOv8 framework ignores labels with such coordinates.
Each annotation file, with the .txt extension,
is named to correspond with its associated image file.
For example, frame_000001.txt serves as the annotation for the
frame_000001.jpg image.
Import
Uploaded file: a zip archive of the same structure as above.
For compatibility with other tools exporting in Ultralytics YOLO format
(e.g. roboflow),
CVAT supports datasets with the inverted directory order of subset and “images” or “labels”,
i.e. both train/images/, images/train/ are valid inputs.
archive.zip/
├── train/
│ ├── images/ # directory with images for train subset
│ │ ├── image1.jpg
│ │ ├── image2.jpg
│ │ └── ...
│ ├── labels/ # directory with annotations for train subset
│ │ ├── image1.txt
│ │ ├── image2.txt
│ │ └── ...