Formats
YOLOv5 PyTorch TXT

YOLOv5 PyTorch TXT

A modified version of YOLO Darknet annotations that adds a YAML file for model config.
Formats

YOLOv5 PyTorch TXT

Below, we show how to convert data to and from

YOLOv5 PyTorch TXT

. We also list popular models that use the

YOLOv5 PyTorch TXT

data format. Our conversion tools are free to use.

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Convert Data to YOLOv5 PyTorch TXT

Use Roboflow to convert
YOLOv5 PyTorch 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

SOC II Type 2 Compliant

Trusted by 250,000+ developers

Convert Data from YOLOv5 PyTorch TXT

Use Roboflow to convert the following formats to
YOLOv5 PyTorch 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

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
YOLOv5 PyTorch TXT
data format
.

What computer vision models use YOLOv5 PyTorch TXT?

The

YOLO-NAS

,

YOLOR

,

YOLOv5

,

models all use the

YOLOv5 PyTorch TXT

data format.

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

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.

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

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

data.yaml
train: ../train/images
val: ../valid/images

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

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