Convert Supervisely Annotations in Two Minutes
Blog post from Roboflow
Jacob Solawetz's blog post provides a detailed guide on converting Supervise.ly annotations to the YOLO Darknet format, enabling users to utilize Supervise.ly data for custom computer vision models outside of its native platform. The process involves downloading annotated datasets from Supervise.ly, which are initially in a custom JSON format, and then converting these annotations into the simpler YOLO Darknet format using Roboflow. This conversion includes creating a class map in a .labels file and specifying bounding box coordinates in individual .txt files. The post also highlights the flexibility of the YOLO Darknet format for training object detectors and suggests using Roboflow's tools for quick and efficient conversion to other annotation formats such as TFRecord, Pascal VOC, and COCO JSON, among others. Additionally, the guide encourages users to leverage their newly formatted data by training a model, particularly recommending a tutorial on training a YOLOv5 object detector.
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