YOLOv8 PyTorch TXT

Overview

In January 2023, Glenn Jocher and the Ultralytics team launched YOLOv8, the latest in the family of YOLO models. The YOLOv8 repository uses the same format as the YOLOv5 model: YOLOv5 PyTorch TXT.

If you're looking to train YOLOv8 (opens in a new tab), Roboflow is the easiest way to get your annotations in this format. We can seamlessly convert 30+ different object detection annotation formats to YOLOv8 TXT and we automatically generate your YAML config file for you.

Format Description

Below, learn the structure of YOLOv8 PyTorch TXT.

Each image has one txt file with a single line for each bounding box. The format of each row is

class_id center_x center_y width height

where fields are space delimited, and the coordinates are normalized from zero to one.

Note: To convert to normalized xywh from pixel values, divide x (and width) by the image's width and divide y (and height) by the image's height.

1 0.617 0.3594420600858369 0.114 0.17381974248927037
1 0.094 0.38626609442060084 0.156 0.23605150214592274
1 0.295 0.3959227467811159 0.13 0.19527896995708155
1 0.785 0.398068669527897 0.07 0.14377682403433475
1 0.886 0.40879828326180256 0.124 0.18240343347639484
1 0.723 0.398068669527897 0.102 0.1609442060085837
1 0.541 0.35085836909871243 0.094 0.16952789699570817
1 0.428 0.4334763948497854 0.068 0.1072961373390558
1 0.375 0.40236051502145925 0.054 0.1351931330472103
1 0.976 0.3927038626609442 0.044 0.17167381974248927

The `data.yaml` file contains configuration values used by the model to locate images and map class names to class_id's.

train: ../train/images
val: ../valid/images

nc: 3
names: ['head', 'helmet', 'person']