Exploring the TACO Dataset [Model Training]

The text discusses a machine learning engineer's challenge in building an autonomous robot that can collect litter on the ground. The engineer is using a data-centric approach to improve the performance of a Mask RCNN model on the TACO dataset, which contains 60 classes and has issues such as class imbalance, similar object classes, small objects, and low labeling quality. To address these challenges, the engineer uses various strategies including re-labeling bad samples, fixing mislabeled classes, labeling new samples, and data augmentation. The engineer focuses on one class, clear plastic bottle, and improves its performance by 47% from the baseline after two iterations of data labeling and model re-training using the Encord Active tool.

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