Formats

YOLOv5 PyTorch TXT

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MODELS
EXAMPLE
Use Roboflow to convert
YOLOv5 PyTorch TXT
to the following formats.
Here are pre-configured models that use
YOLOv5 PyTorch TXT
. 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.
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']