Hybrid Object Detection System Gradient Fine-Tuning
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Solution Overview
Problem
Current object detection algorithms based on deep learning require pre-training and fine-tuning, consuming significant time and resources, and fail to achieve good precision without pre-training, especially in Transformer-based architectures.
Innovation Solution
A hybrid object detection system architecture is introduced, combining convolutional layers and self-attention layers, along with a gradient fine-tuning technique to adjust training gradients, allowing for single-stage training without pre-training, thereby reducing computational demands and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based object detection algorithms are used, then detection accuracy can be improved, but training time and computational resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing the training image to generate a gradient map before actual detection training. This gradient map pre-computation prepares the training data in advance, reducing the computational burden during the main training process and thereby reducing training time while maintaining detection accuracy
Solution Approach 2:
The patent substitutes traditional gradient computation methods with a simplified gradient approximation technique. Instead of computing full gradients through complex backpropagation, the system uses a simplified gradient estimation based on pixel intensity differences, replacing the mechanical computation system with a more efficient approximation method that reduces training time
2Measurement precision
If pre-training is performed to achieve good precision, then detection accuracy improves, but computational resources and training complexity increase
Solution Approach 1:
The patent extracts and removes the pre-training step from the traditional two-stage training process. By using the simplified gradient approximation method and pre-processed gradient maps, the system achieves good detection precision without requiring the separate pre-training phase on large datasets like ImageNet, thereby reducing computational resource consumption
Solution Approach 2:
The patent changes the training parameters by using a simplified gradient computation formula with different weight coefficients (λ1, λ2, λ3) compared to traditional methods. This parameter change allows the system to achieve effective training with fewer computational resources by optimizing the gradient update rule rather than relying on extensive pre-training
3Adaptability or versatility
If Transformer-based architectures are used, then detection capability is enhanced, but dependence on pre-training increases and training complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing gradient maps and preparing training data in advance, which simplifies the training process for Transformer-based architectures. This pre-processing step reduces the complexity of training by providing ready-to-use gradient information, making Transformer training more manageable without requiring extensive pre-training
Solution Approach 2:
The patent substitutes the complex gradient computation mechanism in Transformer training with a simplified gradient approximation method. This replacement reduces the computational complexity and training complexity by using a more efficient gradient estimation approach that is better suited for the specific detection task
Data Source
AI summary
Implementations of the present specification disclose methods, apparatuses, and devices for training an object detection system by using a gradient fine-tuning technique. In one aspect, the method includes: providing a training image as input to the object detection system; processing the training image by the object detection system; determining a gradient norm of each neural network layer based on object annotation data and the detection result corresponding to the training image; and updating, for each neural network layer, parameter values of the neural network layer based on an average of gradient norms and the gradient norm of the neural network layer.