MakeSense provides rich data export options to save the annotation results in a variety of standard formats such as YOLO, VOC XML, and CSV. This multi-format support ensures that the annotated dataset can seamlessly interface with the training process of mainstream deep learning frameworks such as TensorFlow, PyTorch, Keras, etc. The YOLO format is particularly suitable for target detection tasks, while the VOC XML format is widely used for standard datasets such as PASCAL VOC, and the CSV format facilitates data analysis and visualization.
The tool also supports importing existing annotation files for modification and editing, which is very useful for iterative optimization of datasets. Users can upload annotation text files in YOLO format or VOC XML files, and continue to refine annotations or add new annotation objects in MakeSense. This bi-directional data compatibility greatly improves work efficiency and data reuse, avoiding duplication of efforts.
This answer comes from the articleMakeSense: a free-to-use image annotation tool to improve computer vision project efficiencyThe
