Object Detection Training with Progressive Teacher Models
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Solution Overview
Problem
Conventional object detection models face instability and inefficiency due to varying difficulty in training data, leading to excessive focus on difficult samples and high manual labeling costs.
Innovation Solution
A progressive learning method using multiple teacher models to generate varying intensities of label information, starting from simple to complex, to train a student model efficiently without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training data includes both difficult and simple samples without restrictions, then the model can learn from diverse samples, but the learning process excessively focuses on difficult samples causing instability and low learning results
Solution Approach 1:
The training process is segmented into multiple stages with different sampling strategies. Easy samples are used in early stages for stable initialization, while difficult samples are progressively introduced in later stages, preventing any single sample type from dominating the learning process and causing instability
Solution Approach 2:
The model is preliminarily trained on easy samples before encountering difficult samples. This preliminary action establishes a stable baseline performance and prevents the model from becoming overwhelmed by difficult samples from the beginning of training
2Adaptability or versatility
If training data includes both difficult and simple samples without restrictions, then the model can learn from diverse samples, but the learning results are low
Solution Approach 1:
The training process is segmented into multiple stages with different sampling strategies. Easy samples are used in early stages for stable initialization, while difficult samples are progressively introduced in later stages, preventing any single sample type from dominating the learning process and causing instability
Solution Approach 2:
The model is preliminarily trained on easy samples before encountering difficult samples. This preliminary action establishes a stable baseline performance and prevents the model from becoming overwhelmed by difficult samples from the beginning of training
3Measurement precision
If manual labeling is performed for huge amount of training data, then accurate label information is obtained, but it consumes a lot of manual labeling costs and time
Solution Approach 1:
The system uses automatically generated synthetic training data with known ground truth labels, eliminating the need for manual human labeling. The synthetic data generation process automatically provides accurate label information without human intervention, significantly reducing both time and cost while maintaining label accuracy
Data Source
AI summary
An object detection model training apparatus, method and non-transitory computer readable storage medium thereof are provided. The apparatus performs a first object detection on a plurality of training images to generate a piece of first label information corresponding to each of the training images by a first teacher model. The apparatus trains a student model based on the training images and the first label information. The apparatus performs a second object detection on the training images to generate a piece of second label information corresponding to each of the training images by a second teacher model. The apparatus trains the student model based on the training images and the second label information.