Formats
Scaled-YOLOv4 TXT
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

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 0.17167381974248927
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.

Merge 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.

Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.
Object Detection
Deploy with Roboflow
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GPL-3.0
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Formats
Scaled-YOLOv4 TXT

What is Scaled-YOLOv4 TXT?

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.

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Scaled-YOLOv4 TXT

Below, we show how to convert data to and from

Scaled-YOLOv4 TXT

. We also list popular models that use the

Scaled-YOLOv4 TXT

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to Scaled-YOLOv4 TXT

Use Roboflow to convert
Scaled-YOLOv4 TXT
to the following formats.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get a solution with:
‍

Free data conversion

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Convert Data from Scaled-YOLOv4 TXT

Use Roboflow to convert the following formats to
Scaled-YOLOv4 TXT
format.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get:
‍

Free data conversion

SOC II Type 1 Compliant

Trusted by 250,000+ developers

YOLOv8 PyTorch TXT
to
Scaled-YOLOv4 TXT
Pascal VOC XML
to
Scaled-YOLOv4 TXT
COCO JSON
to
Scaled-YOLOv4 TXT
YOLO Darknet TXT
to
Scaled-YOLOv4 TXT
CreateML JSON
to
Scaled-YOLOv4 TXT
SuperAnnotate JSON
to
Scaled-YOLOv4 TXT
LabelMe JSON
to
Scaled-YOLOv4 TXT
Google Cloud AutoML Vision CSV
to
Scaled-YOLOv4 TXT
Sagemaker GroundTruth Manifest
to
Scaled-YOLOv4 TXT
Supervisely JSON
to
Scaled-YOLOv4 TXT
LabelBox JSON
to
Scaled-YOLOv4 TXT
Scale AI JSON
to
Scaled-YOLOv4 TXT
VGG Image Annotator JSON
to
Scaled-YOLOv4 TXT
VGG Image Annotator CSV
to
Scaled-YOLOv4 TXT
LabelBox Video JSON
to
Scaled-YOLOv4 TXT
VoTT CSV
to
Scaled-YOLOv4 TXT
VoTT JSON
to
Scaled-YOLOv4 TXT
IBM Cloud Annotations JSON
to
Scaled-YOLOv4 TXT
Unity Perception JSON
to
Scaled-YOLOv4 TXT
OIDv4 TXT
to
Scaled-YOLOv4 TXT
YOLO Keras TXT
to
Scaled-YOLOv4 TXT
RetinaNet Keras CSV
to
Scaled-YOLOv4 TXT
OpenImages CSV
to
Scaled-YOLOv4 TXT
Tensorflow Object Detection CSV
to
Scaled-YOLOv4 TXT
Multiclass Classification CSV
to
Scaled-YOLOv4 TXT
Cogniac
to
Scaled-YOLOv4 TXT
Kaggle Wheat CSV
to
Scaled-YOLOv4 TXT
Udacity TXT
to
Scaled-YOLOv4 TXT
Marmot XML
to
Scaled-YOLOv4 TXT
Roboflow is a trusted solution for converting and managing your data. Today, over 100,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

Once your data is in Roboflow, just add the link from your dataset and you're ready to go. We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

Below are pre-configured models that use the
Scaled-YOLOv4 TXT
data format
.

What computer vision models use Scaled-YOLOv4 TXT?

The

Scaled YOLOv4

,

models all use the

Scaled-YOLOv4 TXT

data format.

Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.

To see our entire list of computer vision models, check out the Roboflow Model Library.
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']
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