If your labeling tool exported annotations in theYOLO Darknet
format, but you’re trying to use a
model that needsCOCO
annotations, we’ve got you covered. You can convert those in 3 clicks with Roboflow.
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.
Once your account has been created, click Create Dataset.
You might be looking to use YOLOv4 on your own dataset.
Next, you can choose Preprocessing and Augmentation options for your dataset version and then click Generate.
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
Congratulations, you have successfully converted your dataset from
YOLO Darknet TXT
Ready to use your new
To learn how to create COCO JSON yourself from scratch, see our CVAT (object detection annotation tool) tutorial.
Here are some compatible models:
EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.
Detectron2 is model zoo of it's own for computer vision models written in PyTorch. Detectron2 includes all the models that were available in the original Detectron, such as Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose. It also features several new models, including Cascade R-CNN, Panoptic FPN, and TensorMask.