How to improve instance segmentation AP metrics with human knowledge

Im pro ving instance segment ation AP ( aver age precision ) metrics with human knowledge involves lever aging human expert ise to guide the model ' s training and improve its performance . Here are some strateg ies to achieve this : \n \n 1 . An not ated data : Collect a large , high - quality dataset with bound ing boxes and ground truth mask s for the objects of interest . Ens ure that the annotations are accurate and consistent , as this will help the model learn to recognize objects more effectively . \n 2 . Human - in - the - loop ( H IT L ) training : In cor por ate human feedback into the training process by using a H IT L approach . This involves having a human annot ator review the model ' s predictions and provide feedback on the bound ing boxes and mask s . The model can then use this feedback to improve its performance . \n 3 . Active learning : Im plement an active learning strategy where the model selects the most uncertain instances for human annotation . This can help reduce the annotation bur den while still impro ving the model ' s performance . \n 4 . Domain adaptation : If the dataset is not representative of the target domain , consider domain adaptation techniques to transfer knowledge from a source domain with similar objects and scenes . \n 5 . Data augment ation : Aug ment the training data with various transformations , such as random cro pping , f li pping , and rotation , to increase the model ' s robust ness and general ization capabilities . \n 6 . Model architecture : Ex periment with different instance segment ation models and architect ures to find the one that best su its your dataset and task requirements . \n 7 . Post - processing : App ly post - processing techniques , such as non - maxim um supp ression ( N MS ) and threshold ing , to improve the model ' s output and achieve higher AP metrics . \n 8 . E valu ation metrics : Use a combination of AP metrics , such as AP @ 5 0 , AP @ 7 5 , and AP @ 5 0 0 , to evaluate the model ' s performance across different levels of confidence . This can help identify areas where the model needs improvement . \n 9 . Iter ative training : Cont inu ously train and evaluate the model , incorpor ating human feedback and adjust ing the training process as needed . This iter ative approach can help improve the model ' s performance over time . \n 1 0 . Human - in - the - loop inference : Finally , once the model has been trained and optimized , use it in a H IT L inference setting , where a human annot ator reviews the model ' s predictions and provides feedback to improve the output . \n \n By incorpor ating human knowledge and expert ise throughout the training process , you can improve instance segment ation AP metrics and achieve better performance on your target task . </s>