How to Do Data Exploration for Image Segmentation and Object Detection (Things I Had to Learn the Hard Way)

In his blog post, Jakub Cieślik discusses the importance of deliberate data exploration in the fields of image segmentation and object detection within machine learning, a process he argues is often overlooked. He attributes this neglect to a lack of understanding of the models and the perceived complexity of image data exploration tools. Cieślik emphasizes that data exploration is crucial for success, as it allows practitioners to address common challenges such as class imbalances and small object detection, and make informed decisions about preprocessing and augmentation techniques. He also highlights the significance of visualizing datasets and results to uncover insights that metrics alone may not reveal. The post further elaborates on the complexities of handling image dimensions, label sizes, class imbalances, and augmentation in these tasks, stressing the need for a systematic approach to data exploration. Additionally, Cieślik introduces tools like the COCO dataset explorer to streamline data inspection and result evaluation, ultimately advocating for meticulous data exploration to enhance model performance and reliability in real-world applications.