How To Convert RetinaNet Keras CSV to YOLO Darknet TXT

Formats



RetinaNet Keras CSV



Convert to

YOLO Darknet TXT

RetinaNet Keras CSV

A custom format used by the Keras implementation of RetinaNet.





YOLO Darknet TXT

The favored annotation format of the Darknet family of models.

How To Convert RetinaNet Keras CSV to YOLO Darknet TXT

If your labeling tool exported annotations in the

RetinaNet Keras

format, but you’re trying to use a

YOLOv4 Darknet

model that needs

YOLO Darknet

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

RetinaNet Keras CSV

images and annotations into the upload space.

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

YOLO Darknet TXT

Congratulations, you have successfully converted your dataset from

RetinaNet Keras CSV

format to

YOLO Darknet TXT

format!

Next Steps

Ready to use your new

YOLO Darknet

dataset?

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

Here are some compatible models:

YOLOv4 Darknet

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 Darknet.

YOLOv4 Tiny

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset