YOLOv8 PyTorch TXT
to
YOLOv5 PyTorch TXT
In June 2020, Glenn Jocher released a followup to his popular YOLOv3 PyTorch Ultralytics repository and dubbed it YOLOv5. The model uses an annotation format similar to YOLO Darknet TXT but with the addition of a YAML file containing model configuration and class values.
If you're looking to train YOLOv5, Roboflow is the easiest way to get your annotations in this format. We can seamlessly convert 30+ different object detection annotation formats to YOLOv5 TXT and we automatically generate your YAML config file for you. Plus we offer many public datasets already pre-converted for this format.
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
YOLOv5 PyTorch TXT
. We also list popular models that use the
YOLOv5 PyTorch TXT
data format. Our conversion tools are free to use.
The
YOLO-NAS
,
YOLOR
,
YOLOv5
,
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 0.17167381974248927The `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']
