A modified version of YOLO Darknet annotations that adds a YAML file for model config used for training YOLOv10 models. This format is the same as YOLOv8 PyTorch TXT and YOLOv9 PyTorch TXT.
Below, learn the structure of YOLOv10 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 YOLOv10 PyTorch TXT. Read our dedicated guides to learn how to merge and split YOLOv10 PyTorch TXT detections.
Below, see model architectures that require data in the YOLOv10 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.
A modified version of YOLO Darknet annotations that adds a YAML file for model config used for training YOLOv10 models. This format is the same as YOLOv8 PyTorch TXT and YOLOv9 PyTorch TXT.
With Roboflow, you can deploy a computer vision model without having to build your own infrastructure.
Below, we show how to convert data to and from
YOLOv10 PyTorch TXT
. We also list popular models that use the
YOLOv10 PyTorch TXT
data format. Our conversion tools are free to use.
Free data conversion
SOC II Type 2 Compliant
Trusted by 250,000+ developers
Free data conversion
SOC II Type 1 Compliant
Trusted by 250,000+ developers
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']