YOLOv11 PyTorch TXT is the format used by the YOLO11 object detection model.
Below, learn the structure of YOLOv11 PyTorch TXT.
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.
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.
train: ../train/images
val: ../valid/images
nc: 3
names: ['head', 'helmet', 'person']
With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in YOLOv11 PyTorch TXT. Read our dedicated guides to learn how to merge and split YOLOv11 PyTorch TXT detections.
Below, see model architectures that require data in the YOLOv11 PyTorch 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.
YOLOv11 PyTorch TXT is the format used by the YOLO11 object detection model.
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Below, we show how to convert data to and from
YOLOv11 PyTorch TXT
. We also list popular models that use the
YOLOv11 PyTorch TXT
data format. Our conversion tools are free to use.
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The
models all use the
data format.
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.
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.
train: ../train/images
val: ../valid/images
nc: 3
names: ['head', 'helmet', 'person']