Critics may argue against the necessity of a large-scale model like SkySense due to several reasons:
Computational Resources: Critics may point out that training and deploying such a massive model require significant computational resources which might not be feasible for all organizations or research institutions.
Overfitting Concerns: There might be concerns about overfitting when using such a large-scale model on specific tasks with limited datasets. Critics may argue that smaller models trained on task-specific datasets could perform equally well without the complexity of a billion-scale model.
Interpretability: Large-scale models are often criticized for their lack of interpretability compared to smaller models where each component is more easily understood by researchers or users.
4 .Ethical Considerations: Some critics may raise ethical concerns regarding privacy issues related to extensive data collection required for training such large models.