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YOLOv5 PyTorch TXT
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.

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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.

YOLOv5 PyTorch TXT

CONVERT To
CONVERT From
MODELS
EXAMPLE
Use Roboflow to convert
YOLOv5 PyTorch TXT
to the following formats.
Use Roboflow to convert the following formats to
YOLOv5 PyTorch TXT
format.
Pascal VOC XML
to
YOLOv5 PyTorch TXT
COCO JSON
to
YOLOv5 PyTorch TXT
YOLO Darknet TXT
to
YOLOv5 PyTorch TXT
CreateML JSON
to
YOLOv5 PyTorch TXT
Sagemaker GroundTruth Manifest
to
YOLOv5 PyTorch TXT
SuperAnnotate JSON
to
YOLOv5 PyTorch TXT
Google Cloud AutoML Vision CSV
to
YOLOv5 PyTorch TXT
LabelMe JSON
to
YOLOv5 PyTorch TXT
Scale AI JSON
to
YOLOv5 PyTorch TXT
Supervisely JSON
to
YOLOv5 PyTorch TXT
LabelBox JSON
to
YOLOv5 PyTorch TXT
VGG Image Annotator JSON
to
YOLOv5 PyTorch TXT
VGG Image Annotator CSV
to
YOLOv5 PyTorch TXT
VoTT JSON
to
YOLOv5 PyTorch TXT
VoTT CSV
to
YOLOv5 PyTorch TXT
LabelBox Video JSON
to
YOLOv5 PyTorch TXT
IBM Cloud Annotations JSON
to
YOLOv5 PyTorch TXT
Unity Perception JSON
to
YOLOv5 PyTorch TXT
OIDv4 TXT
to
YOLOv5 PyTorch TXT
YOLO Keras TXT
to
YOLOv5 PyTorch TXT
RetinaNet Keras CSV
to
YOLOv5 PyTorch TXT
OpenImages CSV
to
YOLOv5 PyTorch TXT
Tensorflow Object Detection CSV
to
YOLOv5 PyTorch TXT
Kaggle Wheat CSV
to
YOLOv5 PyTorch TXT
Marmot XML
to
YOLOv5 PyTorch TXT
Udacity TXT
to
YOLOv5 PyTorch TXT
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.
YOLOv5

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

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']