Scaled-YOLOv4 TXT
Scaled-YOLOv4 uses a variant on the Darknet TXT format with an additional data.yaml configuration file.
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 0.17167381974248927data.yaml
train: ../train/images
val: ../valid/images
nc: 3
names: ['head', 'helmet', 'person']Convert Data to Scaled-YOLOv4 TXT
- COCO JSON
- Cogniac
- CreateML JSON
- Google Cloud AutoML Vision CSV
- IBM Cloud Annotations JSON
- Kaggle Wheat CSV
- LabelBox JSON
- LabelBox Video JSON
- LabelMe JSON
- Marmot XML
- Multiclass Classification CSV
- OIDv4 TXT
- OpenImages CSV
- Pascal VOC XML
- RetinaNet Keras CSV
- Sagemaker GroundTruth Manifest
- Scale AI JSON
- SuperAnnotate JSON
- Supervisely JSON
- Tensorflow Object Detection CSV
- Udacity TXT
- Unity Perception JSON
- VGG Image Annotator CSV
- VGG Image Annotator JSON
- VoTT CSV
- VoTT JSON
- YOLO Darknet TXT
- YOLO Keras TXT
- YOLOv8 PyTorch TXT