Summary
In conclusion, this chapter has demonstrated how active ML can be applied to optimize the training of computer vision models. As we have seen, computer vision tasks such as image classification, object detection, and instance segmentation require large labeled datasets to train convolutional neural networks (CNNs). Manually collecting and labeling this much data is expensive and time-consuming.
Active ML provides a solution to this challenge by intelligently selecting the most informative examples to be labeled by a human oracle. Strategies such as uncertainty sampling query the model to find the data points it is least certain about. By labeling only these useful data points, we can train our models with significantly less data-labeling effort required.
In this chapter, we covered implementing active ML approach for diverse computer vision applications. By interactively querying the model and refining the training data, we can rapidly improve model performance at a fraction...
