What is the YOLOv5 PyTorch TXT Annotation Format?

Overview

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.

Format Description

Below, learn the structure of YOLOv5 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.

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']

Split and Merge Datasets

With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in YOLOv5 PyTorch TXT. Read our dedicated guides to learn how to merge and split YOLOv5 PyTorch TXT detections.

Split detections in YOLOv5 PyTorch TXT


import supervision as sv

ds = sv.DetectionDataset.from_yolo(
    images_directory_path=f"dataset/train/images",
    annotations_directory_path=f"dataset/train/labels",
    data_yaml_path=f"dataset/data.yaml"
)

train_ds, test_ds = ds.split(split_ratio=0.7,
                             random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)

Supported Models

Below, see model architectures that require data in the YOLOv5 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.

YOLOv5 PyTorch TXT

Overview

Format Description

Split and Merge Datasets

Split detections in YOLOv5 PyTorch TXT

Supported Models

YOLO-NAS

YOLOR

YOLOv5

On this page

Convert Data from YOLOv5 PyTorch TXT

Convert Data to YOLOv5 PyTorch TXT

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