Image Classification Model Training via Segmented Backbone and Parallel Heads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image classification models face challenges in fine-grained classification and multi-label image data, leading to difficulties in extracting detailed features and improving performance due to limited training data and resource constraints.
Innovation Solution
A method involving multiple training steps using supervised and self-supervised learning, multi-task learning, and type 1 and type 2 labeling to train a model body and heads, allowing for feature extraction and classification, with the model body freezing during type 2 labeling, enabling effective fine-grained classification and reducing training time and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image classification models are trained with limited fine-grained labeled data, then the model structure remains simple, but the classification performance for fine-grained categories deteriorates
Solution Approach 1:
The model is segmented into a shared backbone and multiple task-specific heads. Each head is specialized for a particular classification task (fine-grained classification, multi-label classification, etc.), allowing the model to learn different classification objectives simultaneously while sharing feature extraction capabilities in the backbone
Solution Approach 2:
The backbone network serves multiple functions by extracting features that are reused across different classification tasks. The same backbone is used for fine-grained classification, multi-label classification, and other tasks, making the system multi-functional and efficient with limited data
2Reliability
If multi-label training data is used to extract multiple features, then feature extraction capability improves, but the scarcity of multi-labeled data limits performance improvement
Solution Approach 1:
The system employs self-supervised learning where the model generates its own training signals from unlabeled data through data augmentation and consistency regularization. This allows the model to learn robust feature representations without relying on scarce multi-labeled data, serving itself by creating supervisory signals from unsupervised data
3Adaptability or versatility
If multiple training tasks are performed simultaneously, then model versatility improves, but training time and computational resources increase
Solution Approach 1:
The training process is segmented into distinct phases: pre-training on large-scale data, then fine-tuning on specific tasks. Each task head is trained with task-specific loss functions while sharing the backbone, allowing parallel task execution without excessive resource consumption
Solution Approach 2:
The model performs pre-training on large-scale ImageNet data before fine-tuning on specific fine-grained classification tasks. This preliminary action prepares the backbone with general feature extraction capabilities, reducing the training time and resources needed for subsequent task-specific fine-tuning
4Adaptability or versatility
If the model is trained to recognize multiple classes, then classification coverage improves, but the number of required model heads and parameters increases
Solution Approach 1:
Multiple task heads share the same backbone network, allowing the model to handle multiple classification tasks with different numbers of classes without duplicating the entire model architecture. This universal backbone serves all classification functions efficiently
Solution Approach 2:
Each task head has specialized local structure tailored to its specific task (e.g., different output dimensions for different numbers of classes), while the shared backbone maintains general feature extraction quality. This allows each component to have optimal local properties without increasing overall complexity excessively
Data Source
AI summary
An apparatus for training an image classification model according to an embodiment disclosed includes a first trainer that trains a model body and a first head through supervised learning based on a labeled data set subjected to type 1 labeling, a second trainer that trains the model body, the first head, and a second head through multi-task learning based on the labeled data set and an unlabeled data set, and a third trainer that trains a plurality of third heads through supervised learning based on the labeled data set subjected to type 2 labeling while freezing the model body.