ОСОБЛИВОСТІ НЕЗАЛЕЖНОГО НАВЧАННЯ НЕЙРОМЕРЕЖ НА БАЗІ ПЛАТФОРМИ YOLO V8N

The implementation of neural network components for solving specialized tasks under conditions of input data uncertainty is becoming a promising direction in the development of digital technologies. One of the key challenges is the limitation of data available for training, necessitating data manipulation, particularly the use of different sequences of the existing dataset. This approach can contribute to better generalization, as the network is exposed to a greater variety of data combinations. As a result, the model learns to handle input variability and avoids overfitting to a static data order. It is worth noting that data bias often arises when the input sequence remains static. Utilizing different sequences can reduce the risk of the model becoming dependent on patterns inherent in a specific data structure, thereby promoting the formation of more robust data representations. In real-world scenarios, data is often imperfectly ordered or contains distortions. In such cases, varying the input sequences helps the model become more resilient to variability and less dependent on ideal input data structures. Artificially generating different training sequences from a single dataset expands the training sample without increasing its actual size. Additionally, data augmentation techniques can be applied to tasks with limited dataset sizes. Another challenge in neural networks is their excessive confidence in incorrect predictions. One potential solution is the aggregation of multiple model outputs, which helps mitigate this issue, as different models rarely make the same mistake simultaneously. A recognition system based on multiple neural networks with aggregation methods becomes more adaptable to new data types and tasks. Consequently, such a system offers improved scalability, as adding a new model specializing in a narrow subtask allows for seamless integration into the existing system through the aggregation mechanism.