Is it free to convert YOLO Darknet TXT to YOLOv8 PyTorch TXT?
Yes! It is free to convert YOLO Darknet TXT data into the YOLOv8 PyTorch TXT format on the Roboflow platform.
Convert annotation formats
How To Convert YOLO Darknet TXT to YOLOv8 PyTorch TXT
In this guide, we show you how to convert data between the YOLO Darknet and YOLOv8 formats for free. You can use your converted data to train YOLOv8 Classification models and other models that support the YOLOv8 format.
YOLO Darknet TXTtoYOLOv8 PyTorch TXT
YOLO Darknet TXT
The favored annotation format of the Darknet family of models.
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 heightimg0001.txt
1 0.408 0.30266666666666664 0.104 0.15733333333333333
1 0.245 0.424 0.046 0.08darknet.labels
head
helmet
personYOLOv8 PyTorch TXT
A modified version of YOLO Darknet annotations that adds a YAML file for model config.
Below, learn the structure of YOLOv8 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 0.17167381974248927The `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']
Step 1: Create a free Roboflow public workspace
Roboflow is a universal conversion tool for computer vision annotation formats. The Public plan is the best way for those exploring personal projects, class assignments, and other experiments to try Roboflow. To convert your dataset, start by creating a free workspace on the Public plan.
Step 2: Upload your data into Roboflow
Once your account has been created, click Create New Project.
Upload your data to Roboflow by dragging and dropping your YOLO Darknet TXT images and annotations into the upload space.
Step 3: Generate Dataset Version
Next, click "Generate New Version" to generate a new version of your dataset:
You can then apply any preprocessing or augmentation steps to your dataset:
Step 4: Export Dataset Version
After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. Choose YOLOv8 PyTorch TXT when asked in what format you want to export your data. You will see a dropdown with various options like this:
Congratulations, you have successfully converted your dataset from YOLO Darknet TXT format to YOLOv8 PyTorch TXT format!
Video Tutorial
Convert Computer Vision Annotation Formats Tutorial
Want to dive deeper into converting annotation formats with Roboflow? In the tutorial below, we explore how to convert your data in the Roboflow dashboard. We also discuss rejecting annotations and train-test-validation splits.
Frequently Asked Questions
Yes! It is free to convert YOLO Darknet TXT data into the YOLOv8 PyTorch TXT format on the Roboflow platform.
If you have between a few and a few thousand images, converting data between these formats will be quick. But, the time it takes to convert between data formats increases with the more images you have.
Convert YOLO Darknet TXT to other formats
- COCO JSON
- COCO Run-Length Encoding (RLE)
- CreateML JSON
- Florence-2
- Google Cloud AutoML Vision CSV
- meituan/yolov6
- Multiclass Classification CSV
- OpenAI CLIP Classification
- OpenAI GPT-4o Object Detection JSONL
- PaliGemma JSONL
- Pascal VOC XML
- RetinaNet Keras CSV
- Sagemaker GroundTruth Manifest
- Scaled-YOLOv4 TXT
- Tensorflow Object Detection CSV
- Tensorflow TFRecord
- YOLO Keras TXT
- YOLOv10 PyTorch TXT
- YOLOv11 PyTorch TXT
- YOLOv4 PyTorch TXT
- YOLOv5 Oriented Bounding Boxes
- YOLOv5 PyTorch TXT
- YOLOv7 PyTorch TXT
- YOLOv8 Oriented Bounding Boxes
- YOLOv9 PyTorch TXT
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