What is the Scaled-YOLOv4 TXT Annotation Format?

Overview

Scaled-YOLOv4 was released in December 2020 and improves on YOLOv4 and YOLOv5 to achieve state of the art performance on the COCO dataset. It uses the same format as YOLOv5, which is a modified version of YOLO Darknet's TXT annotation format, but we've split it out into a separate download format for clarity.

‍

We have a how to train Scaled-YOLOv4 tutorial available that consumes this format and provide many public datasets in Scaled-YOLOv4 format.

Format Description

Below, learn the structure of Scaled-YOLOv4 TXT.

001.txt

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

data.yaml

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

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

Split and Merge Datasets

With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in Scaled-YOLOv4 TXT. Read our dedicated guides to learn how to merge and split Scaled-YOLOv4 TXT detections.

Split detections in Scaled-YOLOv4 TXT

No items found.

Supported Models

Below, see model architectures that require data in the Scaled-YOLOv4 TXT format when training a new model.

On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.