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5 July 2024 A training strategy to optimize the performance of target detection model
Ninghui He, Pei Zhang, Xiu Zhou, Shitao Liu, Jinpeng Hao, Rui Liu
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846Y (2024) https://doi.org/10.1117/12.3032892
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
Although neural networks have made tremendous progress in feature extraction, each year new models refresh the accuracy of previous models on major data sets, which also shows that there are still a considerable number of effective features in the data set. This paper presents a model and a training strategy to fully exploit the effective features in the data set. The main feature of the model is the use of focal loss based on gradient cropping; the main steps of the strategy include: (1) build a model that contains all necessary optimization techniques, and debug the model capacity to the highest accuracy, get an initial model; (2) corrects the missing and wrong labels of the data set based on the initial model to obtain the training set 1, training the model, and obtaining the model 1; (3) extracting the FN and FP as the training set 2, training the model, and obtaining the model 2; (4) repeating (3) ; (5) NMS is used to merge the prediction results of filtering model 1 to model n. Experiments show that, based on the model obtained by this strategy, MAP increases by 2.7%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ninghui He, Pei Zhang, Xiu Zhou, Shitao Liu, Jinpeng Hao, and Rui Liu "A training strategy to optimize the performance of target detection model", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846Y (5 July 2024); https://doi.org/10.1117/12.3032892
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KEYWORDS
Data modeling
Education and training
Performance modeling
Statistical modeling
Neural networks
Machine learning
Target detection
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