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YOLO Darknet TXT

The favored annotation format of the Darknet family of models.

Overview

This format contains one text file per image (containing the annotations and a numeric representation of the label) and a labelmap which maps the numeric IDs to human readable strings. The annotations are normalized to lie within the range [0, 1] which makes them easier to work with even after scaling or stretching images. It has become quite popular as it has followed the Darknet framework's implementations of the various YOLO models.

Roboflow can read and write YOLO Darknet files so you can easily convert them to or from any other object detection annotation format. Once you're ready, use your converted annotations with our training YOLO v4 with a custom dataset tutorial.

Format Description

Below, learn the structure of YOLO Darknet 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
img0001.txt
1 0.408 0.30266666666666664 0.104 0.15733333333333333
1 0.245 0.424 0.046 0.08
darknet.labels
head
helmet
person

Supported Models

Below, see model architectures that require data in the YOLO Darknet TXT format when training a new model.

  • YOLOv4Object Detection

    YOLOv4 is the original Darknet-based detector by Academia Sinica achieving 43.5% AP on COCO at 65 FPS using CSPDarknet53 and PANet neck.

  • YOLOv4-tinyObject Detection

    YOLOv4-tiny is a lightweight YOLOv4 variant designed for real-time detection on embedded and mobile hardware where GPU acceleration is unavailable.

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