Standardized design of data output
The CSV file generated by Annot8 strictly follows the common specification for machine learning datasets and contains three core fields: image path, label name, and bounding box coordinates. This structured output can be directly used for object detection model training in mainstream frameworks such as TensorFlow, PyTorch, etc., eliminating the need for additional data preprocessing steps.
Framework Adaptation Details
- Supports the normalized coordinate format required by the YOLO family of models.
- Compatible with some of the annotation specifications of the COCO dataset
- Configurable export options to accommodate input requirements for different frameworks
- Provide multi-label support for complex scenarios and tasks.
Practical tests show that the data exported by Annot8 can be seamlessly interfaced on the TensorFlow Object Detection API, and the accuracy metrics are comparable to the manually labeled dataset.
This answer comes from the articleAnnot8: Quickly Labeling Images to Train AI ModelsThe
May not be reproduced without permission:AI productivity tools " Annot8's CSV export format is compatible with major machine learning frameworks.
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