Suggested Features for Automatic Annotation

[pandaTED]

1. Model Orchestration: The ability to orchestrate the sequence of models being called. For example, first enhance the images, then apply YOLO-Seg, and finally use SAM;
2. Model Evaluation Visualization: A feature to visualize model evaluation. This would involve using the trained model to automatically annotate the validation set and then display the results of both manual and automatic annotations side by side for easy comparison.

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1. 模型编排：可以编排调用的模型顺序。例如，先对图片进行增强，然后调用 YOLO-Seg，最后调用 SAM；
2. 模型评估可视化：一个用于可视化模型评估的功能。这将涉及使用训练好的模型自动标注验证集，并同时展示人工标注和自动标注的结果，以方便比对。

[CVHub520]

@pandaTED

Hi, there! Thank you for your suggestions and interest in X-AnyLabeling! 🙌

Regarding the two features you've suggested, here are some thoughts:

1. Model Orchestration:

   • The idea of orchestrating different models to enhance the annotation process is indeed intriguing and has the potential to significantly improve efficiency and accuracy.
   • However, as you mentioned, the compatibility issues between different models, especially in terms of preprocessing, postprocessing, and input/output formats, pose a significant challenge.
   • We are aware of these challenges and will continue to monitor developments in this area for potential solutions. We also welcome contributions from the community in terms of ideas and practical experiences.

2. Model Evaluation Visualization:

   • The visualization of model evaluation is a crucial aspect of the training cycle. 📈
   • However, the primary focus of X-AnyLabeling is to provide high-quality annotated data before the training phase, whereas model evaluation typically occurs after training.
   • Therefore, this feature might not align fully with the tool's current scope. 😕 We suggest considering other specialized model evaluation tools that are designed to fulfill this need. 💡

Finallly, we highly value user feedback and are committed to continuously improving our product. Thank you again for your suggestions, and we welcome you to keep sharing your valuable insights. 🌟

Best regards, CVHub