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Formats
YOLO Darknet TXT
Convert to
YOLOv4 PyTorch TXT
YOLO Darknet TXT
The favored annotation format of the Darknet family of models.
YOLOv4 PyTorch TXT
A format used with the PyTorch port of YOLO v4.

How To Convert YOLO Darknet TXT to YOLOv4 PyTorch TXT

If your labeling tool exported annotations in the

YOLO Darknet

format, but you’re trying to use a

YOLOv4 PyTorch

model that needs

YOLOv4 Pytorch

annotations, we’ve got you covered. You can convert those in 3 clicks with Roboflow.

Step 1: Create a Free Roboflow Public Workspace

Roboflow is the 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 workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create Dataset.

Upload your data to Roboflow by dragging and dropping your
YOLO Darknet TXT
images and annotations into the upload space.
Upload Data To Roboflow

You might be looking to use YOLOv4 on your own dataset.

Step 3: Generate Dataset Version

Next, you can choose Preprocessing and Augmentation options for your dataset version and then click Generate.

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. You can also choose which format you would like to export. Choose

YOLOv4 PyTorch TXT

.

Congratulations, you have successfully converted your dataset from

YOLO Darknet TXT

format to

YOLOv4 PyTorch TXT

format!

Next Steps

Ready to use your new

YOLOv4 Pytorch

dataset?

To learn more about YOLOv4 PyTorch, check it out in our model library.

Here are some compatible models:

YOLOv4 PyTorch

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.